{ "name": "RigBase: measured local LLM speeds on home AI hardware", "license": "https://creativecommons.org/licenses/by/4.0/", "attribution": "RigBase (getrigbase.com), CC BY 4.0. 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"https://www.reddit.com/r/LocalLLaMA/comments/1hdpvpt/llama_33_speed/", "unverified": true, "page_url": "https://getrigbase.com/builds/single-4060ti-16gb-64gb-ram/" }, { "slug": "rtx-4090-llamacpp-cuda", "title": "Single RTX 4090 (llama.cpp CUDA scoreboard)", "gpus": "RTX 4090 24GB", "vram_gb": 24, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "llama-bench, flash attention on", "cost": null, "currency": "", "power": "", "results": [ { "model": "Llama 2 7B", "quant": "Q4_0", "engine": "llama.cpp CUDA (FA)", "tps": 188.96, "spec_decode": "none" } ], "source": "https://github.com/ggml-org/llama.cpp/discussions/15013", "page_url": "https://getrigbase.com/builds/rtx-4090-llamacpp-cuda/" }, { "slug": "rtx-4090-koboldcpp-comparison", "title": "RTX 4090 desktop (speed comparison vs Macs)", "gpus": "RTX 4090 24GB", "vram_gb": 24, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "Total T/s incl. prompt processing, no FA", "cost": null, "currency": "", "power": "", 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"MXFP4", "engine": "llama.cpp", "tps": 79.68, "spec_decode": "none" } ], "source": "https://github.com/ggml-org/llama.cpp/discussions/4167", "source2": "https://github.com/XiongjieDai/GPU-Benchmarks-on-LLM-Inference", "source3": "https://github.com/ggml-org/llama.cpp/discussions/15396", "page_url": "https://getrigbase.com/builds/m2-ultra-76c-192gb/" }, { "slug": "macbook-pro-m2-max", "title": "MacBook Pro M2 Max", "gpus": "Apple M2 Max", "vram_gb": null, "cpu": "Apple M2 Max", "motherboard": "", "ram": "", "psu": "", "other": "Total T/s incl. prompt processing", "cost": null, "currency": "", "power": "", "results": [ { "model": "Llama 3.1 8B", "quant": "Q8", "engine": "llama.cpp-based", "tps": 28.92, "spec_decode": "none" } ], "source": "https://www.someoddcodeguy.dev/low-context-speed-comparison-macbook-mac-studios-and-rtx-4090/", "page_url": "https://getrigbase.com/builds/macbook-pro-m2-max/" }, { "slug": "mac-studio-m1-ultra", "title": "Mac Studio M1 Ultra", "gpus": "Apple M1 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"https://getrigbase.com/builds/macbook-m3-max-40c-64gb/" }, { "slug": "macbook-m1-max-32c-64gb", "title": "MacBook Pro M1 Max 32-core GPU, 64 GB", "gpus": "Apple M1 Max 32-core GPU", "vram_gb": 64, "cpu": "Apple M1 Max", "motherboard": "", "ram": "64 GB unified", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Llama 3 8B", "quant": "Q4_K_M", "engine": "llama.cpp", "tps": 34.49, "spec_decode": "none" } ], "source": "https://github.com/XiongjieDai/GPU-Benchmarks-on-LLM-Inference", "page_url": "https://getrigbase.com/builds/macbook-m1-max-32c-64gb/" }, { "slug": "epyc-512gb-cpu-only-deepseek", "title": "$2K EPYC 64-core, 512 GB DDR4, CPU-only", "gpus": "None (CPU inference)", "vram_gb": 0, "cpu": "AMD EPYC 64-core (7702 / 7C13 / 7V13)", "motherboard": "Gigabyte MZ32-AR0", "ram": "512 GB DDR4", "psu": "850W", "other": "Samsung 980 Pro 1TB, rack frame, Corsair H170i cooler", "cost": 2000, "currency": "USD", "power": "", "results": [ { "model": "DeepSeek R1 671B", "quant": "Q4", "engine": "llama.cpp (bare metal Ubuntu 24)", "tps": 4.31, "spec_decode": "none" } ], "source": "https://www.youtube.com/watch?v=Tq_cmN4j2yY", "page_url": "https://getrigbase.com/builds/epyc-512gb-cpu-only-deepseek/" }, { "slug": "m3-ultra-512gb-deepseek", "title": "Mac Studio M3 Ultra, 512 GB", "gpus": "Apple M3 Ultra", "vram_gb": 512, "cpu": "Apple M3 Ultra", "motherboard": "", "ram": "512 GB unified", "psu": "", "other": "MLX; drops to 6.39 t/s at 13k-token prompt", "cost": null, "currency": "", "power": "", "results": [ { "model": "DeepSeek R1 671B", "quant": "Q4", "engine": "MLX", "tps": 18.43, "spec_decode": "none" } ], "source": "https://www.reddit.com/r/LocalLLaMA/comments/1j9vjf1/deepseek_r1_671b_q4_m3_ultra_512gb_with_mlx/", "unverified": true, "page_url": "https://getrigbase.com/builds/m3-ultra-512gb-deepseek/" }, { "slug": "framework-desktop-strix-halo", "title": "Framework Desktop (AMD Strix Halo) mainboard", "gpus": "AMD Ryzen AI Max+ 395 (Radeon 8060S iGPU; Ollama runs CPU only, llama.cpp runs on iGPU via Vulkan)", "vram_gb": null, "cpu": "AMD Ryzen AI Max (Strix Halo)", "motherboard": "Framework Desktop mainboard", "ram": "", "psu": "", "other": "Ollama benchmark", "cost": null, "currency": "", "power": "~141 W during Ollama CPU runs; ~133 W during llama.cpp Vulkan iGPU runs (Llama 3.2 3B, Llama 3.1 70B)", "results": [ { "model": "DeepSeek R1 Distill 8B", "quant": "Ollama default", "engine": "Ollama (CPU only, iGPU not used)", "tps": 19.49, "spec_decode": "none" }, { "model": "DeepSeek R1 Distill 70B", "quant": "Ollama default", "engine": "Ollama (CPU only, iGPU not used)", "tps": 2.43, "spec_decode": "none" }, { "model": "Llama 3.1 70B", "quant": "Q4_K_M", "engine": "llama.cpp Vulkan (iGPU, llama-bench tg128)", "tps": 4.97, "spec_decode": "none" }, { "model": "Llama 3.2 3B", "quant": "Q4_K_M", "engine": "llama.cpp Vulkan (iGPU, llama-bench tg128)", "tps": 88.14, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/issues/21", "source2": "https://github.com/geerlingguy/ai-benchmarks/issues/21#issuecomment-3164568218", "source3": "https://github.com/geerlingguy/ai-benchmarks", "page_url": "https://getrigbase.com/builds/framework-desktop-strix-halo/" }, { "slug": "4x3090-4u-am5", "title": "4x RTX 3090 in a 4U rack case (AM5)", "gpus": "4x Dell OEM RTX 3090", "vram_gb": 96, "cpu": "AMD Ryzen 9 7850X3D", "motherboard": "Asus ProArt X670E-Creator WiFi", "ram": "128 GB DDR5 4800", "psu": "Corsair HX1500i + Corsair RM850e", "other": "Inter-Tech 4W40 case, c-payne SlimSAS PCIe gen4 adapters, risers", "cost": 5835, "currency": "EUR", "power": "", "results": [], "source": "https://www.reddit.com/r/LocalLLaMA/comments/1in69s3/4x3090_in_a_4u_case_dont_recommend_it/", "unverified": true, "page_url": "https://getrigbase.com/builds/4x3090-4u-am5/" }, { "slug": "4x3090-threadripper-2920x-homelab", "title": "4x RTX 3090 homelab on Threadripper 2920X", "gpus": "4x RTX 3090 (~$625 each, used)", "vram_gb": 96, "cpu": "AMD Threadripper 2920X", "motherboard": "MSI X399 Gaming Pro Carbon AC", "ram": "128 GB DDR4 3200", "psu": "be quiet! Dark Power Pro 13 1300W", "other": "Open mining-rig frame, PCIe risers", "cost": 3300, "currency": "USD", "power": "", "results": [], "source": "https://www.reddit.com/r/LocalLLaMA/comments/1ha3hwu/my_homelab_build_4x_rtx_3090_powerhouse/", "unverified": true, "page_url": "https://getrigbase.com/builds/4x3090-threadripper-2920x-homelab/" }, { "slug": "4x3090-tr32-192gb-qwen3-235b", "title": "4x RTX 3090 + 32-core Threadripper, 192 GB DDR4", "gpus": "4x RTX 3090", "vram_gb": 96, "cpu": "AMD Threadripper 32-core", "motherboard": "", "ram": "192 GB DDR4", "psu": "", "other": "MoE expert offload to CPU", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3 235B-A22B", "quant": "UD-Q3_K_XL", "engine": "llama.cpp", "tps": "12-14", "spec_decode": "none" } ], "note": "Author reports 12-14 t/s; midpoint shown.", "source": "https://www.reddit.com/r/LocalLLaMA/comments/1khmaah/5_commands_to_run_qwen3235ba22b_q3_inference_on/", "unverified": true, "page_url": "https://getrigbase.com/builds/4x3090-tr32-192gb-qwen3-235b/" }, { "slug": "4x3090-epyc-7443-256gb", "title": "4x RTX 3090 + EPYC 7443, 256 GB DDR4 8-channel", "gpus": "4x RTX 3090", "vram_gb": 96, "cpu": "AMD EPYC 7443 (24 cores)", "motherboard": "", "ram": "256 GB DDR4 3200 (8 channel)", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3 235B-A22B", "quant": "IQ4_XS", "engine": "llama.cpp", "tps": 18, "spec_decode": "none" } ], "source": "https://www.reddit.com/r/LocalLLaMA/comments/1khmaah/5_commands_to_run_qwen3235ba22b_q3_inference_on/", "unverified": true, "page_url": "https://getrigbase.com/builds/4x3090-epyc-7443-256gb/" }, { "slug": "epyc-7f52-6gpu-136gb", "title": "EPYC 7F52 jank rig: 4x3090 + 4090 + 4080 (136 GB VRAM)", "gpus": "4x RTX 3090, RTX 4090, RTX 4080", "vram_gb": 136, "cpu": "AMD EPYC 7F52", "motherboard": "ASRock Rack ROMED8-2T", "ram": "32 GB RDIMM 3200", "psu": "SuperFlower 1600W + 1200W server PSU", "other": "WD Black 4TB NVMe", "cost": null, "currency": "", "power": "", "results": [ { "model": "Llama 3.1 70B", "quant": "Q8_0", "engine": "Oobabooga", "tps": 9, "spec_decode": "none" } ], "source": "https://www.reddit.com/r/LocalLLaMA/comments/1fqwler/show_me_your_ai_rig/", "unverified": true, "page_url": "https://getrigbase.com/builds/epyc-7f52-6gpu-136gb/" }, { "slug": "epyc-plus-3090-deepseek-q2", "title": "EPYC + single RTX 3090 (hybrid)", "gpus": "RTX 3090", "vram_gb": 24, "cpu": "AMD EPYC", "motherboard": "", "ram": "", "psu": "", "other": "llama-bench", "cost": null, "currency": "", "power": "", "results": [ { "model": "DeepSeek R1 671B", "quant": "Q2_K", "engine": "llama.cpp CUDA", "tps": 8.97, "spec_decode": "none" } ], "source": "https://www.reddit.com/r/LocalLLaMA/comments/1j2c7b4/unsloth_ds_r1_222b_2_ts_on_dell_r730_cpu_help/", "unverified": true, "page_url": "https://getrigbase.com/builds/epyc-plus-3090-deepseek-q2/" }, { "slug": "intel-265k-rtx-4090", "title": "Intel Core Ultra 265K + RTX 4090", "gpus": "RTX 4090", "vram_gb": 24, "cpu": "Intel Core Ultra 7 265K", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "Peak 509.1 W (gpt-oss 20B), 464.2 W (DeepSeek R1 14B)", "results": [ { "model": "gpt-oss 20B", "quant": "", "engine": "llama.cpp", "tps": 279.65, "spec_decode": "none" }, { "model": "DeepSeek R1 Distill 14B", "quant": "", "engine": "llama.cpp", "tps": 92.49, "spec_decode": "none" }, { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 334.67, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/issues/43", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/intel-265k-rtx-4090/" }, { "slug": "intel-265k-rtx-3080-ti", "title": "Intel Core Ultra 265K + RTX 3080 Ti", "gpus": "RTX 3080 Ti", "vram_gb": 12, "cpu": "Intel Core Ultra 7 265K", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "Peak 495 W", "results": [ { "model": "DeepSeek R1 Distill 14B", "quant": "", "engine": "llama.cpp", "tps": 70.74, "spec_decode": "none" }, { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 263.24, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/issues/39", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/intel-265k-rtx-3080-ti/" }, { "slug": "intel-265k-radeon-ai-pro-r9700", "title": "Intel Core Ultra 265K + Radeon AI Pro R9700", "gpus": "AMD Radeon AI Pro R9700", "vram_gb": 32, "cpu": "Intel Core Ultra 7 265K", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "Peak 332.9 W (gpt-oss 20B)", "results": [ { "model": "gpt-oss 20B", "quant": "", "engine": "llama.cpp", "tps": 163.26, "spec_decode": "none" }, { "model": "DeepSeek R1 Distill 14B", "quant": "", "engine": "llama.cpp", "tps": 53.72, "spec_decode": "none" }, { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 195.91, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/issues/35", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/intel-265k-radeon-ai-pro-r9700/" }, { "slug": "intel-265k-rtx-4070-ti", "title": "Intel Core Ultra 265K + RTX 4070 Ti", "gpus": "RTX 4070 Ti", "vram_gb": 12, "cpu": "Intel Core Ultra 7 265K", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "Peak 298.7 W (gpt-oss 20B)", "results": [ { "model": "gpt-oss 20B", "quant": "", "engine": "llama.cpp", "tps": 163.14, "spec_decode": "none" }, { "model": "DeepSeek R1 Distill 14B", "quant": "", "engine": "llama.cpp", "tps": 49.74, "spec_decode": "none" }, { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 196.15, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/issues/45", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/intel-265k-rtx-4070-ti/" }, { "slug": "intel-265k-rtx-2080-ti", "title": "Intel Core Ultra 265K + RTX 2080 Ti", "gpus": "RTX 2080 Ti", "vram_gb": 11, "cpu": "Intel Core Ultra 7 265K", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "Peak 307.8 W (DeepSeek R1 14B)", "results": [ { "model": "DeepSeek R1 Distill 14B", "quant": "", "engine": "llama.cpp", "tps": 45.06, "spec_decode": "none" }, { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 180.38, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/issues/46", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/intel-265k-rtx-2080-ti/" }, { "slug": "intel-265k-rtx-3060", "title": "Intel Core Ultra 265K + RTX 3060", "gpus": "RTX 3060", "vram_gb": 12, "cpu": "Intel Core Ultra 7 265K", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "Peak 229.3 W (gpt-oss 20B)", "results": [ { "model": "gpt-oss 20B", "quant": "", "engine": "llama.cpp", "tps": 83.96, "spec_decode": "none" }, { "model": "DeepSeek R1 Distill 14B", "quant": "", "engine": "llama.cpp", "tps": 29.77, "spec_decode": "none" }, { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 122.85, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/issues/40", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/intel-265k-rtx-3060/" }, { "slug": "intel-265k-rtx-a4000", "title": "Intel Core Ultra 265K + RTX A4000", "gpus": "RTX A4000", "vram_gb": 16, "cpu": "Intel Core Ultra 7 265K", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "Peak 199.4 W (gpt-oss 20B)", "results": [ { "model": "gpt-oss 20B", "quant": "", "engine": "llama.cpp", "tps": 93.55, "spec_decode": "none" }, { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 149.44, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/issues/36", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/intel-265k-rtx-a4000/" }, { "slug": "mac-studio-m3-ultra-512gb-geerling", "title": "Mac Studio M3 Ultra 512GB (Geerling test)", "gpus": "Apple M3 Ultra (unified)", "vram_gb": 512, "cpu": "Apple M3 Ultra", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "Peak 261.8 W (DeepSeek R1 671B), 243 W (Llama 3.1 70B), 227 W (gpt-oss 20B), 223 W (Llama 3.2 3B)", "results": [ { "model": "DeepSeek R1 671B", "quant": "", "engine": "llama.cpp", "tps": 19.89, "spec_decode": "none" }, { "model": "gpt-oss 20B", "quant": "", "engine": "llama.cpp", "tps": 115.29, "spec_decode": "none" }, { "model": "gpt-oss 20B", "quant": "MXFP4", "engine": "llama.cpp (Metal, llama-bench)", "tps": 115.52, "spec_decode": "none" }, { "model": "Llama 3.1 70B", "quant": "", "engine": "llama.cpp", "tps": 14.08, "spec_decode": "none" }, { "model": "Llama 3.1 405B", "quant": "", "engine": "llama.cpp", "tps": 1.96, "spec_decode": "none" }, { "model": "DeepSeek R1 Distill 14B", "quant": "", "engine": "llama.cpp", "tps": 51.85, "spec_decode": "none" }, { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 154.6, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/issues/42", "source2": "https://github.com/ggml-org/llama.cpp/discussions/15396", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test. The 115.52 MXFP4 figure is from the llama.cpp gpt-oss guide.", "source3": "https://github.com/geerlingguy/ai-benchmarks", "page_url": "https://getrigbase.com/builds/mac-studio-m3-ultra-512gb-geerling/" }, { "slug": "mac-studio-m1-ultra-64c-128gb", "title": "Mac Studio M1 Ultra (64-core GPU) 128GB", "gpus": "Apple M1 Ultra 64-core GPU (unified)", "vram_gb": 128, "cpu": "Apple M1 Ultra", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "gpt-oss 20B", "quant": "", "engine": "llama.cpp", "tps": 100.43, "spec_decode": "none" }, { "model": "DeepSeek R1 Distill 14B", "quant": "", "engine": "llama.cpp", "tps": 41.51, "spec_decode": "none" }, { "model": "Llama 3.1 70B", "quant": "", "engine": "llama.cpp", "tps": 9.84, "spec_decode": "none" }, { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 127.6, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/issues/48", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/mac-studio-m1-ultra-64c-128gb/" }, { "slug": "dell-pro-max-gb10-dgx-spark", "title": "Dell Pro Max with GB10 (Nvidia DGX Spark)", "gpus": "Nvidia GB10 (unified)", "vram_gb": null, "cpu": "Nvidia GB10", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "Peak 122.8 W (gpt-oss 20B), 128.5 W (Llama 3.2 3B), 156 W (Llama 3.1 70B)", "results": [ { "model": "gpt-oss 20B", "quant": "", "engine": "llama.cpp", "tps": 87.97, "spec_decode": "none" }, { "model": "DeepSeek R1 Distill 14B", "quant": "", "engine": "llama.cpp", "tps": 23.92, "spec_decode": "none" }, { "model": "Llama 3.1 70B", "quant": "", "engine": "llama.cpp", "tps": 4.71, "spec_decode": "none" }, { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 97.93, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/issues/34", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/dell-pro-max-gb10-dgx-spark/" }, { "slug": "intel-13900k-rtx-5090", "title": "Intel i9-13900K + RTX 5090", "gpus": "RTX 5090", "vram_gb": 32, "cpu": "Intel Core i9-13900K", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 271.4, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/pull/18", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/intel-13900k-rtx-5090/" }, { "slug": "ryzen-7900x-rtx-4090", "title": "Ryzen 9 7900X + RTX 4090", "gpus": "RTX 4090", "vram_gb": 24, "cpu": "AMD Ryzen 9 7900X", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 237.05, "spec_decode": "none" }, { "model": "Llama 3.1 70B", "quant": "", "engine": "GPU+CPU offload", "tps": 3.1, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/pull/11", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/ryzen-7900x-rtx-4090/" }, { "slug": "ryzen-7950x-rtx-4080", "title": "Ryzen 9 7950X + RTX 4080", "gpus": "RTX 4080", "vram_gb": 16, "cpu": "AMD Ryzen 9 7950X", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 204.45, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/pull/11", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/ryzen-7950x-rtx-4080/" }, { "slug": "ryzen-7950x-rtx-4070-ti-super", "title": "Ryzen 9 7950X + RTX 4070 Ti Super", "gpus": "RTX 4070 Ti Super", "vram_gb": 16, "cpu": "AMD Ryzen 9 7950X", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 198.95, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/pull/11", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/ryzen-7950x-rtx-4070-ti-super/" }, { "slug": "ryzen-9950x-rx-7900-xt", "title": "Ryzen 9 9950X + Radeon RX 7900 XT", "gpus": "AMD Radeon RX 7900 XT", "vram_gb": 20, "cpu": "AMD Ryzen 9 9950X", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 131.2, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/pull/11", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/ryzen-9950x-rx-7900-xt/" }, { "slug": "m4-mac-mini-32gb", "title": "M4 Mac mini (10-core) 32GB", "gpus": "Apple M4 (unified)", "vram_gb": 32, "cpu": "Apple M4", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "Peak 30.1 W", "results": [ { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 41.31, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/issues/2", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/m4-mac-mini-32gb/" }, { "slug": "ampereone-a192-512gb-cpu", "title": "AmpereOne A192-32X 512GB (CPU only)", "gpus": "None (CPU only)", "vram_gb": null, "cpu": "AmpereOne A192-32X", "motherboard": "", "ram": "512 GB", "psu": "", "other": "", "cost": null, "currency": "", "power": "Peak 477 W (DeepSeek R1 671B)", "results": [ { "model": "DeepSeek R1 671B", "quant": "", "engine": "llama.cpp", "tps": 4.18, "spec_decode": "none" }, { "model": "Llama 3.1 70B", "quant": "", "engine": "llama.cpp", "tps": 3.86, "spec_decode": "none" }, { "model": "Llama 3.1 405B", "quant": "", "engine": "llama.cpp", "tps": 0.9, "spec_decode": "none" }, { "model": "Llama 3.2 3B", "quant": "", "engine": "llama.cpp", "tps": 23.52, "spec_decode": "none" } ], "source": "https://github.com/geerlingguy/ai-benchmarks/issues/10", "source2": "https://github.com/geerlingguy/ai-benchmarks", "note": "Numbers from Jeff Geerling's ai-benchmarks results table (llama.cpp/Ollama eval rate). Quantization not stated in the summary table. Power is peak whole-system draw during that test.", "page_url": "https://getrigbase.com/builds/ampereone-a192-512gb-cpu/" }, { "slug": "macbook-m4-max-36gb", "title": "Apple M4 Max 36GB", "gpus": "Apple M4 Max (unified)", "vram_gb": 36, "cpu": "Apple M4 Max", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "gpt-oss 20B", "quant": "MXFP4", "engine": "llama.cpp", "tps": 92.36, "spec_decode": "none" } ], "source": "https://github.com/ggml-org/llama.cpp/discussions/15396", "note": "llama-bench tg128 from the official llama.cpp gpt-oss guide (MXFP4, flash attention on). Machine model (MacBook Pro vs Mac Studio) not stated.", "page_url": "https://getrigbase.com/builds/macbook-m4-max-36gb/" }, { "slug": "apple-m1-max-64gb-gpt-oss", "title": "Apple M1 Max 64GB", "gpus": "Apple M1 Max (unified)", "vram_gb": 64, "cpu": "Apple M1 Max", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "gpt-oss 20B", "quant": "MXFP4", "engine": "llama.cpp", "tps": 75.15, "spec_decode": "none" } ], "source": "https://github.com/ggml-org/llama.cpp/discussions/15396", "note": "llama-bench tg128 from the official llama.cpp gpt-oss guide (MXFP4, flash attention on). Machine model not stated.", "page_url": "https://getrigbase.com/builds/apple-m1-max-64gb-gpt-oss/" }, { "slug": "rtx-pro-6000-96gb", "title": "RTX Pro 6000 96GB workstation card", "gpus": "RTX Pro 6000 (Blackwell)", "vram_gb": 96, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "gpt-oss 120B", "quant": "MXFP4", "engine": "llama.cpp", "tps": 196.31, "spec_decode": "none" }, { "model": "gpt-oss 20B", "quant": "MXFP4", "engine": "llama.cpp", "tps": 286.91, "spec_decode": "none" } ], "source": "https://github.com/ggml-org/llama.cpp/discussions/15396", "note": "llama-bench tg128 from the official llama.cpp gpt-oss guide (MXFP4, flash attention on). GPU-only entry: rest of the system not stated.", "page_url": "https://getrigbase.com/builds/rtx-pro-6000-96gb/" }, { "slug": "single-rtx-3090-gpt-oss", "title": "Single RTX 3090 24GB", "gpus": "RTX 3090", "vram_gb": 24, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "gpt-oss 20B", "quant": "MXFP4", "engine": "llama.cpp", "tps": 161.77, "spec_decode": "none" } ], "source": "https://github.com/ggml-org/llama.cpp/discussions/15396", "note": "llama-bench tg128 from the official llama.cpp gpt-oss guide (MXFP4, flash attention on). GPU-only entry: rest of the system not stated.", "page_url": "https://getrigbase.com/builds/single-rtx-3090-gpt-oss/" }, { "slug": "single-rtx-5090-gpt-oss", "title": "Single RTX 5090 32GB", "gpus": "RTX 5090", "vram_gb": 32, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "gpt-oss 20B", "quant": "MXFP4", "engine": "llama.cpp", "tps": 282.51, "spec_decode": "none" } ], "source": "https://github.com/ggml-org/llama.cpp/discussions/15396", "note": "llama-bench tg128 from the official llama.cpp gpt-oss guide (MXFP4, flash attention on). GPU-only entry: rest of the system not stated.", "page_url": "https://getrigbase.com/builds/single-rtx-5090-gpt-oss/" }, { "slug": "single-rtx-5070-ti-gpt-oss", "title": "Single RTX 5070 Ti 16GB", "gpus": "RTX 5070 Ti", "vram_gb": 16, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "gpt-oss 20B", "quant": "MXFP4", "engine": "llama.cpp", "tps": 189.45, "spec_decode": "none" } ], "source": "https://github.com/ggml-org/llama.cpp/discussions/15396", "note": "llama-bench tg128 from the official llama.cpp gpt-oss guide (MXFP4, flash attention on). GPU-only entry: rest of the system not stated.", "page_url": "https://getrigbase.com/builds/single-rtx-5070-ti-gpt-oss/" }, { "slug": "single-rtx-5060-ti-16gb-gpt-oss", "title": "Single RTX 5060 Ti 16GB", "gpus": "RTX 5060 Ti 16GB", "vram_gb": 16, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "gpt-oss 20B", "quant": "MXFP4", "engine": "llama.cpp", "tps": 111.51, "spec_decode": "none" } ], "source": "https://github.com/ggml-org/llama.cpp/discussions/15396", "note": "llama-bench tg128 from the official llama.cpp gpt-oss guide (MXFP4, flash attention on). GPU-only entry: rest of the system not stated.", "page_url": "https://getrigbase.com/builds/single-rtx-5060-ti-16gb-gpt-oss/" }, { "slug": "single-rtx-5080-gpt-oss", "title": "Single RTX 5080 16GB", "gpus": "RTX 5080", "vram_gb": 16, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "gpt-oss 20B", "quant": "MXFP4", "engine": "llama.cpp", "tps": 204.85, "spec_decode": "none" } ], "source": "https://github.com/ggml-org/llama.cpp/discussions/15396", "note": "llama-bench tg128 from the official llama.cpp gpt-oss guide (MXFP4, flash attention on). GPU-only entry: rest of the system not stated.", "page_url": "https://getrigbase.com/builds/single-rtx-5080-gpt-oss/" }, { "slug": "llamabench-rtx-5090", "title": "RTX 5090: 2026 models (llamabench community)", "gpus": "RTX 5090", "vram_gb": 32, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 258, "spec_decode": "none" }, { "model": "Qwen3.6 27B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 75.7, "spec_decode": "none" }, { "model": "Gemma 4 31B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 70.0, "spec_decode": "none" }, { "model": "Gemma 4 26B-A4B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 235, "spec_decode": "none" }, { "model": "Gemma 4 12B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 142, "spec_decode": "none" }, { "model": "gpt-oss 20B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 313, "spec_decode": "none" }, { "model": "Qwen3.5 9B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 208, "spec_decode": "none" } ], "source": "https://llamabench.ai/browse/rtx5090", "note": "Community llama-bench results aggregated by llamabench.ai (decode tok/s, short context). Only runs without speculative decoding listed unless noted. Open the source for exact quant and llama.cpp version. ", "page_url": "https://getrigbase.com/builds/llamabench-rtx-5090/" }, { "slug": "llamabench-rtx-4090", "title": "RTX 4090: 2026 models (llamabench community)", "gpus": "RTX 4090", "vram_gb": 24, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 27B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 47.0, "spec_decode": "none" }, { "model": "Gemma 4 31B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 43.8, "spec_decode": "none" }, { "model": "Gemma 4 26B-A4B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 164, "spec_decode": "none" }, { "model": "Gemma 4 12B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 98.1, "spec_decode": "none" }, { "model": "gpt-oss 20B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 203, "spec_decode": "none" }, { "model": "Qwen3.5 9B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 137, "spec_decode": "none" }, { "model": "LFM2.5 24B-A2B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": "261-339", "spec_decode": "none" }, { "model": "Nemotron 3.5 Bolt 74B-A9B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 108, "spec_decode": "none" } ], "source": "https://llamabench.ai/browse/rtx4090", "note": "Community llama-bench results aggregated by llamabench.ai (decode tok/s, short context). Only runs without speculative decoding listed unless noted. Open the source for exact quant and llama.cpp version.", "page_url": "https://getrigbase.com/builds/llamabench-rtx-4090/" }, { "slug": "llamabench-rtx-3090", "title": "RTX 3090: 2026 models (llamabench community)", "gpus": "RTX 3090", "vram_gb": 24, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": "146-153", "spec_decode": "none" }, { "model": "Qwen3.6 27B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 40.2, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": "38.2-71.6", "spec_decode": "none" } ], "source": "https://llamabench.ai/browse/rtx3090", "note": "Community llama-bench results aggregated by llamabench.ai (decode tok/s, short context). Only runs without speculative decoding listed unless noted. Open the source for exact quant and llama.cpp version. Qwen3.6 27B and Qwen3.8 27B rows include runs with MTP/draft speculative decoding.", "page_url": "https://getrigbase.com/builds/llamabench-rtx-3090/" }, { "slug": "llamabench-rtx-5070-ti", "title": "RTX 5070 Ti: 2026 models (llamabench community)", "gpus": "RTX 5070 Ti", "vram_gb": 16, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Gemma 4 12B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 87.6, "spec_decode": "none" } ], "source": "https://llamabench.ai/browse/rtx5070ti", "note": "Community llama-bench results aggregated by llamabench.ai (decode tok/s, short context). Only runs without speculative decoding listed unless noted. Open the source for exact quant and llama.cpp version.", "page_url": "https://getrigbase.com/builds/llamabench-rtx-5070-ti/" }, { "slug": "llamabench-rtx-3060", "title": "RTX 3060 12GB: 2026 models (llamabench community)", "gpus": "RTX 3060", "vram_gb": 12, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Gemma 4 12B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 38.5, "spec_decode": "none" }, { "model": "LFM2.5 24B-A2B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": 125, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": "5.2-6.7", "spec_decode": "none" } ], "source": "https://llamabench.ai/browse/rtx3060", "note": "Community llama-bench results aggregated by llamabench.ai (decode tok/s, short context). Only runs without speculative decoding listed unless noted. Open the source for exact quant and llama.cpp version. Qwen3.8 27B and LFM2.5 partially offloaded to CPU.", "page_url": "https://getrigbase.com/builds/llamabench-rtx-3060/" }, { "slug": "llamabench-rtx-4060-ti-16gb", "title": "RTX 4060 Ti 16GB: 2026 models (llamabench community)", "gpus": "RTX 4060 Ti 16GB", "vram_gb": 16, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "LFM2.5 24B-A2B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": "131-150", "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": "10.7-17.6", "spec_decode": "none" } ], "source": "https://llamabench.ai/browse/rtx4060ti16gb", "note": "Community llama-bench results aggregated by llamabench.ai (decode tok/s, short context). Only runs without speculative decoding listed unless noted. Open the source for exact quant and llama.cpp version. Partially offloaded to CPU; Qwen3.8 rows use speculative decoding.", "page_url": "https://getrigbase.com/builds/llamabench-rtx-4060-ti-16gb/" }, { "slug": "llamabench-radeon-8060s-strix-halo", "title": "Strix Halo (Radeon 8060S, 96 GB): 2026 models (llamabench community)", "gpus": "AMD Ryzen AI Max+ (Radeon 8060S iGPU)", "vram_gb": 96, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "llama-bench tg128 (quant per result)", "engine": "llama.cpp", "tps": "50.2-62.4", "spec_decode": "none" } ], "source": "https://llamabench.ai/browse/radeon8060s", "note": "Community llama-bench results aggregated by llamabench.ai (decode tok/s, short context). Only runs without speculative decoding listed unless noted. Open the source for exact quant and llama.cpp version.", "page_url": "https://getrigbase.com/builds/llamabench-radeon-8060s-strix-halo/" }, { "slug": "rtx-3090-qwen36-35b-a3b-giles", "title": "Single RTX 3090: Qwen3.6 35B-A3B (Giles Thomas)", "gpus": "RTX 3090", "vram_gb": 24, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "Q4 GGUF, all layers on GPU, ~90K ctx", "engine": "llama.cpp CUDA", "tps": 139.6, "spec_decode": "none" } ], "source": "https://www.gilesthomas.com/2026/07/benchmarking-qwen-3-6-35b-moe-rtx-3090", "note": "At full 262K context with 12 layers of FFNs offloaded to CPU: ~66 tok/s.", "page_url": "https://getrigbase.com/builds/rtx-3090-qwen36-35b-a3b-giles/" }, { "slug": "4x3090-qwen36-vllm-tp2", "title": "4x RTX 3090 lab: Qwen3.6 on vLLM vs llama.cpp", "gpus": "4x RTX 3090", "vram_gb": 96, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "IQ4_XS (1 GPU)", "engine": "llama.cpp", "tps": "115-133", "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "AWQ-INT4 (TP=2)", "engine": "vLLM", "tps": 149, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "AWQ-INT4 + MTP-3 (TP=2)", "engine": "vLLM", "tps": "225-231", "spec_decode": "MTP" } ], "source": "https://github.com/tfriedel/qwen3.6-rtx3090-lab", "page_url": "https://getrigbase.com/builds/4x3090-qwen36-vllm-tp2/" }, { "slug": "mac-studio-m3-ultra-512gb-muse-spark-open", "title": "Mac Studio M3 Ultra 512GB: Muse Spark Open 816B (Hugging Face test)", "gpus": "Apple M3 Ultra (unified)", "vram_gb": 512, "cpu": "Apple M3 Ultra", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Muse Spark Open 816B-A42B", "quant": "Q2_K (imatrix), ~304 GB", "engine": "llama.cpp", "tps": 18, "spec_decode": "none" }, { "model": "Muse Spark Open 816B-A42B", "quant": "oQ3e (3-bit), ~344 GB", "engine": "MLX (oMLX / mlx-lm)", "tps": 21, "spec_decode": "none" } ], "source": "https://huggingface.co/blog/meta-muse-spark-open", "note": "Hugging Face's Muse Spark Open launch post (15 Oct 2026). The MLX figure used a 1,024-token prompt and 128 generated tokens (~225 tok/s prefill); prompt length for the llama.cpp figure is not stated. Meta's 816B MoE has ~42B active parameters, so it needs the full 512 GB Mac.", "page_url": "https://getrigbase.com/builds/mac-studio-m3-ultra-512gb-muse-spark-open/" }, { "slug": "mac-studio-m5-ultra-512gb-apple-mlx", "title": "Mac Studio M5 Ultra 512GB: Qwen3.8 27B and Mistral Medium 3.5 (Apple MLX benchmark)", "gpus": "Apple M5 Ultra (unified)", "vram_gb": 512, "cpu": "Apple M5 Ultra", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "Peak ~300 W (Apple, 65K-token context)", "results": [ { "model": "Qwen3.8 27B", "quant": "BF16, 8,192-token context", "engine": "MLX (mlx_lm.benchmark)", "tps": 19.69, "spec_decode": "none" }, { "model": "Mistral Medium 3.5 128B", "quant": "FP8, 8,192-token context", "engine": "MLX (mlx_lm.benchmark)", "tps": 7.7, "spec_decode": "none" } ], "source": "https://machinelearning.apple.com/research/mlx-distributed-inference", "note": "Apple Machine Learning Research, 15 Oct 2026. Vendor benchmark: decode speed over 128 generated tokens after an 8,192-token context, single user. Apple says M5 Ultra has 1.2 TB/s memory bandwidth vs 819 GB/s on M3 Ultra.", "page_url": "https://getrigbase.com/builds/mac-studio-m5-ultra-512gb-apple-mlx/" }, { "slug": "mac-studio-m3-ultra-512gb-apple-mlx", "title": "Mac Studio M3 Ultra 512GB: Qwen3.8 27B and Mistral Medium 3.5 (Apple MLX benchmark)", "gpus": "Apple M3 Ultra (unified)", "vram_gb": 512, "cpu": "Apple M3 Ultra", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "Peak ~200 W (Apple, 65K-token context)", "results": [ { "model": "Qwen3.8 27B", "quant": "BF16, 8,192-token context", "engine": "MLX (mlx_lm.benchmark)", "tps": 12.46, "spec_decode": "none" }, { "model": "Mistral Medium 3.5 128B", "quant": "FP8, 8,192-token context", "engine": "MLX (mlx_lm.benchmark)", "tps": 4.95, "spec_decode": "none" } ], "source": "https://machinelearning.apple.com/research/mlx-distributed-inference", "note": "Apple Machine Learning Research, 15 Oct 2026, previous-generation baseline. Decode speed over 128 generated tokens after an 8,192-token context, single user.", "page_url": "https://getrigbase.com/builds/mac-studio-m3-ultra-512gb-apple-mlx/" }, { "slug": "2x-mac-studio-m5-ultra-rdma-cluster", "title": "2x Mac Studio M5 Ultra cluster (Thunderbolt 5 RDMA): Muse Spark Open 816B", "gpus": "2x Apple M5 Ultra (unified, Mac Studio)", "vram_gb": null, "cpu": "2x Apple M5 Ultra", "motherboard": "", "ram": "", "psu": "", "other": "Two Mac Studios linked by two Thunderbolt 5 cables (RDMA over Thunderbolt, macOS 26.2+, MLX jaccl backend)", "cost": null, "currency": "", "power": "", "results": [ { "model": "Muse Spark Open 816B-A42B", "quant": "NVFP4, 8,192-token context", "engine": "MLX, 2-node tensor parallel", "tps": 24.99, "spec_decode": "none" }, { "model": "Muse Spark Open 816B-A42B", "quant": "NVFP4, 8,192-token context", "engine": "MLX, 2-node pipeline parallel", "tps": 15.74, "spec_decode": "none" }, { "model": "Mistral Medium 3.5 128B", "quant": "FP8, 8,192-token context", "engine": "MLX, 2-node tensor parallel", "tps": 13.69, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "BF16, 8,192-token context", "engine": "MLX, 2-node tensor parallel", "tps": 33.27, "spec_decode": "none" } ], "source": "https://machinelearning.apple.com/research/mlx-distributed-inference", "note": "Apple Machine Learning Research, 15 Oct 2026 (vendor benchmark, single user, 128 generated tokens). Apple does not state the memory of each Mac in the cluster runs; Muse Spark Open needs ~493 GB in NVFP4 so it does not fit one machine.", "page_url": "https://getrigbase.com/builds/2x-mac-studio-m5-ultra-rdma-cluster/" }, { "slug": "4x-mac-studio-m5-ultra-rdma-cluster", "title": "4x Mac Studio M5 Ultra cluster (Thunderbolt 5 RDMA): Muse Spark Open at 36.7 tok/s", "gpus": "4x Apple M5 Ultra (unified, Mac Studio)", "vram_gb": null, "cpu": "4x Apple M5 Ultra", "motherboard": "", "ram": "", "psu": "", "other": "Four Mac Studios in a Thunderbolt 5 mesh, two cables per pair (RDMA over Thunderbolt, MLX jaccl backend)", "cost": null, "currency": "", "power": "", "results": [ { "model": "Muse Spark Open 816B-A42B", "quant": "NVFP4, 8,192-token context", "engine": "MLX, 4-node tensor parallel", "tps": 36.73, "spec_decode": "none" }, { "model": "Mistral Medium 3.5 128B", "quant": "FP8, 8,192-token context", "engine": "MLX, 4-node tensor parallel", "tps": 23.37, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "BF16, 8,192-token context", "engine": "MLX, 4-node tensor parallel", "tps": 50.89, "spec_decode": "none" } ], "source": "https://machinelearning.apple.com/research/mlx-distributed-inference", "note": "Apple Machine Learning Research, 15 Oct 2026 (vendor benchmark, single user, 128 generated tokens). Apple does not state the memory of each Mac in the cluster runs.", "page_url": "https://getrigbase.com/builds/4x-mac-studio-m5-ultra-rdma-cluster/" }, { "slug": "rtx-3090-qwen38-27b-openjet-megakernel", "title": "Single RTX 3090: Qwen3.8 27B at 78-140 tok/s with the OpenJet megakernel vs llama.cpp", "gpus": "RTX 3090", "vram_gb": 24, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.8 27B", "quant": "Q4_K_M (unsloth), greedy code prompt", "engine": "OpenJet megakernel (MTP, 4 drafts)", "tps": 139.7, "spec_decode": "MTP" }, { "model": "Qwen3.8 27B", "quant": "Q4_K_M (unsloth), greedy code prompt", "engine": "llama.cpp (MTP, 2 drafts)", "tps": 72.5, "spec_decode": "MTP" }, { "model": "Qwen3.8 27B", "quant": "Q4_K_M (unsloth), reasoning mode", "engine": "OpenJet megakernel (MTP)", "tps": 78.0, "spec_decode": "MTP" }, { "model": "Qwen3.8 27B", "quant": "Q4_K_M (unsloth), reasoning mode", "engine": "llama.cpp (MTP, 2 drafts)", "tps": 50.9, "spec_decode": "MTP" }, { "model": "Qwen3.8 27B", "quant": "Q4_K_M (unsloth), no speculation", "engine": "llama.cpp", "tps": 40.4, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "Q4_K_M (unsloth), no speculation", "engine": "OpenJet megakernel", "tps": 42.9, "spec_decode": "none" } ], "source": "https://github.com/L-Forster/open-jet/tree/master/megakernel", "note": "From the OpenJet megakernel README (r/LocalLLaMA post, Oct 2026). Single conversation, back-to-back on an idle GPU. The 139.7/72.5 figures are the best-case 'Python function' greedy prompt; the 4-prompt greedy mean is 106.7 vs 63.1 tok/s. Speedups come from speculative decoding (MTP); without it both engines run ~40-43 tok/s. Works only with specific Qwen3.8-27B Q4_K_M / UD-Q4_K_XL GGUFs.", "page_url": "https://getrigbase.com/builds/rtx-3090-qwen38-27b-openjet-megakernel/" }, { "slug": "hp-omnibook-ultra-16-rtx-spark-128gb", "title": "HP OmniBook Ultra 16 (RTX Spark N1X, 128GB) laptop: local AI benchmarks", "gpus": "Nvidia RTX Spark N1X, 6,144-core Blackwell GPU (unified)", "vram_gb": 128, "cpu": "Nvidia RTX Spark N1X, 20-core Grace CPU", "motherboard": "", "ram": "128GB LPDDR5X-9400 (64GB GPU / 64GB system split, 62.5GB GPU-usable)", "psu": "", "other": "2TB Sandisk SN8050S Gen5 SSD; Windows 11 Pro 26H1 (Arm64)", "cost": 6229.99, "currency": "USD", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "Ollama default, 64K context, Codex CLI coding agent", "engine": "Ollama", "tps": 89.5, "spec_decode": "none" }, { "model": "gpt-oss 20B", "quant": "Ollama default, 64K context, Codex CLI coding agent", "engine": "Ollama", "tps": 58.0, "spec_decode": "none" }, { "model": "Llama 3.1 8B", "quant": "MLPerf Client v2.0 (geomean)", "engine": "llama.cpp CUDA", "tps": 56.5, "spec_decode": "none" } ], "source": "https://www.storagereview.com/review/hp-omnibook-ultra-16-review-rtx-spark-n1x", "note": "StorageReview review, 16 Oct 2026. Cost is HP.com's configurator price for this 128GB/2TB configuration as of 14 Oct 2026, not a builder's receipt. Generation rates are from an agentic coding task and MLPerf Client, not llama-bench. The 32GB model ran Llama 3.1 8B at the same speed (56.4) but its 12GB GPU split can't load the larger models.", "page_url": "https://getrigbase.com/builds/hp-omnibook-ultra-16-rtx-spark-128gb/" }, { "slug": "surface-laptop-ultra-rtx-spark-microcenter", "title": "Microsoft Surface Laptop Ultra (RTX Spark N1X): llama.cpp speeds on 2026 models (Micro Center)", "gpus": "Nvidia RTX Spark N1X, 6,144-core Blackwell GPU (unified)", "vram_gb": null, "cpu": "Nvidia RTX Spark N1X, 20-core Grace CPU", "motherboard": "", "ram": "", "psu": "", "other": "", "cost": null, "currency": "", "power": "Avg GPU power while generating: 43-48 W", "results": [ { "model": "Nemotron 3 Nano Omni 30B", "quant": "general, short (~90-token) prompt", "engine": "llama.cpp (Nvidia ARM64 build)", "tps": 63.9, "spec_decode": "none" }, { "model": "GLM 4.7 Flash", "quant": "general, short prompt", "engine": "llama.cpp (Nvidia ARM64 build)", "tps": 55.6, "spec_decode": "none" }, { "model": "Qwen3.5 122B", "quant": "A10B, general, short prompt", "engine": "llama.cpp (Nvidia ARM64 build)", "tps": 35.2, "spec_decode": "none" }, { "model": "Muse Glimmer 30B", "quant": "general, short prompt", "engine": "llama.cpp (Nvidia ARM64 build)", "tps": 15.9, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "general, short prompt", "engine": "llama.cpp (Nvidia ARM64 build)", "tps": 14.5, "spec_decode": "none" } ], "source": "https://www.microcenter.com/site/mc-news/article/benchmarking-the-rtx-spark.aspx", "note": "Micro Center benchmarks, 15 Oct 2026. Long (~9,000-token) prompts were within a few tok/s (e.g. Qwen3.8 27B 14.1, Qwen3.5 122B 34.0). The article does not state the tested unit's memory configuration or quantization.", "page_url": "https://getrigbase.com/builds/surface-laptop-ultra-rtx-spark-microcenter/" }, { "slug": "dgx-spark-nemotron-35-super-vl-bolt", "title": "DGX Spark (GB10, 128GB): Nemotron 3.5 Super VL 120B and Bolt 74B, launch-week runs", "gpus": "Nvidia GB10 (unified)", "vram_gb": 128, "cpu": "Nvidia GB10", "motherboard": "", "ram": "128GB unified memory", "psu": "", "other": "", "cost": null, "currency": "", "power": "GPU peak 82 W (nvidia-smi) during Super VL runs with prompts up to 64K tokens", "results": [ { "model": "Nemotron 3.5 Super VL 120B-A12B", "quant": "NVFP4, no speculative decoding, 256-token output", "engine": "vLLM", "tps": 19.06, "spec_decode": "none" }, { "model": "Nemotron 3.5 Super VL 120B-A12B", "quant": "NVFP4 + LiLiCorr speculative decoding (7 draft tokens)", "engine": "vLLM", "tps": 38.07, "spec_decode": "LiLiCorr" }, { "model": "Nemotron 3.5 Bolt 74B-A9B", "quant": "2-bit 24GB GGUF, no speculative decoding", "engine": "llama.cpp", "tps": 39.47, "spec_decode": "none" }, { "model": "Nemotron 3.5 Bolt 74B-A9B", "quant": "2-bit 24GB GGUF + DFlash speculative decoding (7 draft tokens)", "engine": "llama.cpp", "tps": 61.61, "spec_decode": "DFlash" } ], "source": "https://dev.classmethod.jp/articles/dgx-spark-nemotron-3-5-super-vl-first-touch/", "source2": "https://dev.classmethod.jp/articles/dgx-spark-nemotron-3-5-bolt-74b-a9b-first-touch/", "source3": "https://forums.developer.nvidia.com/t/nemotron-3-5-super-vl-120b-a12b-nvfp4-on-a-single-dgx-spark-34-49-tok-s-decode-with-lilicorr-16k-thermal-shutdowns-at-32k/385991", "note": "DevelopersIO (Classmethod) articles of 16 and 18 Oct 2026, measured on the author's own DGX Spark with the model cards' Spark recipes; median of 3 runs, temperature 0. Super VL ran in vLLM (nightly image from the model card), Bolt in llama.cpp. Speculative-decoding speeds depend on how predictable the text is. The NVIDIA forum thread (third link) reports hard power-offs on some Sparks during long (32K+) Super VL prompts.", "page_url": "https://getrigbase.com/builds/dgx-spark-nemotron-35-super-vl-bolt/" }, { "slug": "r9700-qwen36-35b-a3b-amdai", "title": "Radeon AI Pro R9700 (one card): Qwen3.6 35B-A3B from 4K to 256K context (AMDai lab rig)", "gpus": "AMD Radeon AI Pro R9700 (1 of 2 cards in the rig used)", "vram_gb": 32, "cpu": "", "motherboard": "", "ram": "256 GB DDR5", "psu": "", "other": "Second R9700 not used for these single-card runs", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "Unsloth UD-Q5_K_XL, short context, f16 KV", "engine": "llama.cpp Vulkan", "tps": 102.39, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "UD-Q5_K_XL, 32K context", "engine": "llama.cpp Vulkan", "tps": 90.52, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "UD-Q5_K_XL, 128K context", "engine": "llama.cpp Vulkan", "tps": 66.43, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "UD-Q5_K_XL, 256K context", "engine": "llama.cpp Vulkan", "tps": 44.4, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "Q5_K_M (24.68 GB VRAM)", "engine": "llama.cpp Vulkan", "tps": 109.73, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "Q4_K_M", "engine": "llama.cpp Vulkan", "tps": 117.69, "spec_decode": "none" } ], "source": "https://amdai.net/", "note": "AMDai lab notes, 'measured on our rig, 2026-10' (gfx1201, llama.cpp Vulkan, 0% system-RAM offload). The site lists the overnight sweep as pending re-verification after a DDR5 fix. Same rig: two single-card copies served about 100 tok/s per request.", "page_url": "https://getrigbase.com/builds/r9700-qwen36-35b-a3b-amdai/" }, { "slug": "r9700-qwen36-27b-mtp-backends", "title": "Radeon AI Pro R9700: Qwen3.6 27B on Vulkan, HIP and Ollama, with and without MTP", "gpus": "AMD Radeon AI Pro R9700 (1 of 2 cards in the rig used)", "vram_gb": 32, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "Second R9700 not used (no tensor splitting)", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 27B", "quant": "Q4_K_M + MTP draft head (draft-n-max 3), 64K context", "engine": "llama.cpp Vulkan", "tps": 66.0, "spec_decode": "MTP" }, { "model": "Qwen3.6 27B", "quant": "Q4_K_M, no MTP", "engine": "llama.cpp Vulkan", "tps": 31.9, "spec_decode": "none" }, { "model": "Qwen3.6 27B", "quant": "Q4_K_M", "engine": "llama.cpp HIP (ROCm)", "tps": 27.1, "spec_decode": "none" }, { "model": "Qwen3.6 27B", "quant": "Q4_K_M", "engine": "Ollama (HIP)", "tps": 26.7, "spec_decode": "none" } ], "source": "https://localaifrontier.com/benchmarks/qwen3-6-27b-mtp-backend-showdown-r9700/", "note": "Local AI Frontier, measured 2026-06-30 on one coding prompt (merge sort), 256-512 generated tokens, temperature 0, best of 3 runs per backend. Treat backend ordering as the takeaway rather than exact speeds.", "page_url": "https://getrigbase.com/builds/r9700-qwen36-27b-mtp-backends/" }, { "slug": "r9700-qwen38-27b-vaditaslim", "title": "Radeon AI Pro R9700: Qwen3.8 27B on llama.cpp, Ollama and vLLM (Vadi Taslim)", "gpus": "AMD Radeon AI Pro R9700 (1 of 2 cards in the rig used)", "vram_gb": 32, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "ROCm 7.x; GPU P2P broken, so every run used one GPU", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.8 27B", "quant": "Q4_K_M + MTP speculative decoding, reasoning on", "engine": "llama.cpp Vulkan", "tps": 57, "spec_decode": "MTP" }, { "model": "Qwen3.8 27B", "quant": "Q4_K_M", "engine": "Ollama", "tps": 30, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "AWQ INT4, single request", "engine": "vLLM (radiance)", "tps": 22, "spec_decode": "none" } ], "source": "https://www.vaditaslim.com/blog/ai/qwen3.8-27b-two-rigs", "note": "Blog post of 17 Aug 2026. 'Raw' generation speed including hidden reasoning tokens; the author measured about half of that as visible answer text (29 tok/s on llama.cpp). vLLM radiance reached 55 tok/s total at 4 concurrent requests.", "page_url": "https://getrigbase.com/builds/r9700-qwen38-27b-vaditaslim/" }, { "slug": "rtx-pro-6000-maxq-qwen38-27b-vaditaslim", "title": "RTX Pro 6000 Blackwell Max-Q (300 W cap): Qwen3.8 27B on vLLM, NVFP4 vs BF16", "gpus": "RTX Pro 6000 Max-Q (Blackwell, 96 GB)", "vram_gb": 96, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "CUDA 13.0; model on local XFS", "cost": null, "currency": "", "power": "GPU power cap 300 W", "results": [ { "model": "Qwen3.8 27B", "quant": "NVFP4 (W4A16) + MTP (5 draft tokens), 256K context", "engine": "vLLM 0.27.1", "tps": 150, "spec_decode": "MTP" }, { "model": "Qwen3.8 27B", "quant": "BF16 + MTP (5 draft tokens), 256K context", "engine": "vLLM 0.20.1", "tps": 85, "spec_decode": "MTP" } ], "source": "https://www.vaditaslim.com/blog/ai/qwen3.8-27b-two-rigs", "note": "Blog post of 17 Aug 2026. 'Raw' speed including hidden reasoning tokens; visible answer text ran at 84 (NVFP4) and 43 (BF16) tok/s. Max-Q is the 300 W blower variant of the RTX Pro 6000, so no street price is attached here.", "page_url": "https://getrigbase.com/builds/rtx-pro-6000-maxq-qwen38-27b-vaditaslim/" }, { "slug": "rx-7900-xtx-qwen36-35b-a3b-mtp", "title": "Radeon RX 7900 XTX (24 GB): Qwen3.6 35B-A3B up to 245K context, with MTP", "gpus": "AMD Radeon RX 7900 XTX", "vram_gb": 24, "cpu": "AMD Ryzen 5 3600", "motherboard": "", "ram": "128 GiB", "psu": "", "other": "llama.cpp ROCm build be4a6a6", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "UD-Q4_K_XL, llama-bench 2K, f16 KV", "engine": "llama.cpp ROCm", "tps": 87.2, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "UD-Q4_K_XL, ~16K real-text context, q4_0 KV", "engine": "llama.cpp ROCm (llama-server)", "tps": 79.6, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "UD-Q4_K_XL + MTP (draft-n-max 4), ~16K context", "engine": "llama.cpp ROCm (llama-server)", "tps": 135.2, "spec_decode": "MTP" }, { "model": "Qwen3.6 35B-A3B", "quant": "UD-Q4_K_XL, ~64K context", "engine": "llama.cpp ROCm (llama-server)", "tps": 66.0, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "UD-Q4_K_XL + MTP, ~64K context", "engine": "llama.cpp ROCm (llama-server)", "tps": 108.2, "spec_decode": "MTP" }, { "model": "Qwen3.6 35B-A3B", "quant": "UD-Q4_K_XL, ~245K context, q4_0 KV", "engine": "llama.cpp ROCm (llama-server)", "tps": 38.3, "spec_decode": "none" } ], "source": "https://gist.github.com/AshyIsMe/ef9dc0e57d7af5626f3935f55c38543a", "note": "GitHub gist created 26 Jun 2026. All layers on the GPU (21.27 GiB model). Only q4_0 KV fits the full 256K context; MTP fits up to about 200K. The same gist measured the dense 27B at 29 tok/s plain and 50 with MTP at 16K.", "page_url": "https://getrigbase.com/builds/rx-7900-xtx-qwen36-35b-a3b-mtp/" }, { "slug": "strix-halo-corsair-ai-300-rocm-vs-vulkan", "title": "Strix Halo (Corsair AI Workstation 300, 128 GB): ROCm vs Vulkan on Qwen3.8 27B and Qwen3.6 35B-A3B", "gpus": "AMD Ryzen AI Max+ 395 (Radeon 8060S iGPU)", "vram_gb": 128, "cpu": "AMD Ryzen AI Max+ 395", "motherboard": "Corsair AI Workstation 300", "ram": "128 GiB (8 x 16 GB, 'LPDDR5' per dmidecode, 8000 MT/s configured)", "psu": "", "other": "Ubuntu 24.04, ROCm 7.14, Mesa RADV 26.1.7, llama.cpp c060ca974", "cost": null, "currency": "", "power": "Balanced power profile", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "Q8_0, llama-bench tg128", "engine": "llama.cpp Vulkan", "tps": 54.22, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "Q8_0, llama-bench tg128", "engine": "llama.cpp ROCm", "tps": 47.47, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "Q8_0 + MTP (draft-n-max 3), coding workload", "engine": "llama.cpp ROCm", "tps": 60.92, "spec_decode": "MTP" }, { "model": "Qwen3.8 27B", "quant": "Q8_0, llama-bench tg128", "engine": "llama.cpp ROCm", "tps": 7.76, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "Q8_0, llama-bench tg128", "engine": "llama.cpp Vulkan", "tps": 7.77, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "Q8_0 + MTP (draft-n-max 3), coding workload, reasoning on", "engine": "llama.cpp ROCm", "tps": 15.67, "spec_decode": "MTP" }, { "model": "Qwen3.8 27B", "quant": "Q8_0 + MTP (draft-n-max 3), coding workload, reasoning on", "engine": "llama.cpp Vulkan", "tps": 13.19, "spec_decode": "MTP" } ], "source": "https://howquant.dev/benchmarks/strix-halo-rocm-vs-vulkan/", "note": "How Quant, first published 24 Aug 2026, updated 16 Sep 2026. llama-bench means over 5 runs; MTP rows are llama-server runs on SPEED-Bench's coding workload (Vulkan MoE + MTP measured 60.40). Same llama.cpp commit for both backends.", "page_url": "https://getrigbase.com/builds/strix-halo-corsair-ai-300-rocm-vs-vulkan/" }, { "slug": "strix-halo-qwen38-27b-mtp-sleepingrobots", "title": "Strix Halo (Ryzen AI Max+ 395, 128 GB): Qwen3.8 27B Q6_K, 9.6 to 18 tok/s with MTP", "gpus": "AMD Ryzen AI Max+ 395 (Radeon 8060S iGPU)", "vram_gb": 128, "cpu": "AMD Ryzen AI Max+ 395", "motherboard": "", "ram": "128 GB LPDDR5X unified memory", "psu": "", "other": "Fedora 44, llama-vulkan-radv toolbox", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.8 27B", "quant": "Q6_K, no MTP", "engine": "llama.cpp Vulkan (RADV)", "tps": 9.59, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "Q6_K + MTP (draft-n-max 2)", "engine": "llama.cpp Vulkan (RADV)", "tps": 18.01, "spec_decode": "MTP" }, { "model": "Qwen3.8 27B", "quant": "Q6_K + MTP (draft-n-max 5)", "engine": "llama.cpp Vulkan (RADV)", "tps": 15.16, "spec_decode": "MTP" } ], "source": "https://sleepingrobots.com/dreams/qwen38-mtp-strix-halo/", "note": "Sleeping Robots, 15 Aug 2026. 1 warmup + 3 measured runs, fixed prompt, 256 output tokens. AMDVLK gave the same base decode speed (9.59) but much slower prompt processing.", "page_url": "https://getrigbase.com/builds/strix-halo-qwen38-27b-mtp-sleepingrobots/" }, { "slug": "dgx-spark-vllm-23-models-ctala-oct-2026", "title": "DGX Spark (GB10, 128 GB): 23 models on vLLM at 128K context, Qwen3.6 35B-A3B up to 102 tok/s", "gpus": "Nvidia GB10 (unified)", "vram_gb": 128, "cpu": "Nvidia GB10 Grace (Arm)", "motherboard": "", "ram": "128 GB LPDDR5X unified (~121 GB usable)", "psu": "", "other": "NVIDIA DGX Spark, DGX OS (aarch64), FP8 KV cache, 128K context per conversation", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "NVFP4 + MTP (k=2)", "engine": "vLLM 0.31.0, 128K ctx, 1 user", "tps": 102.3, "spec_decode": "MTP" }, { "model": "Qwen3.6 35B-A3B", "quant": "NVFP4, no speculative decoding", "engine": "vLLM 0.31.0, 128K ctx, 1 user", "tps": 78.1, "spec_decode": "none" }, { "model": "gpt-oss 20B", "quant": "", "engine": "vLLM 0.31.0, 128K ctx, 1 user", "tps": 96.2, "spec_decode": "none" }, { "model": "Nemotron 3.5 Lightning 30B-A3B", "quant": "+ DSpark speculative decoding (k=3)", "engine": "vLLM 0.31.0, 128K ctx, 1 user", "tps": 94.4, "spec_decode": "DSpark" }, { "model": "Nemotron 3.5 Lightning 30B-A3B", "quant": "no speculative decoding", "engine": "vLLM 0.31.0, 128K ctx, 1 user", "tps": 70.3, "spec_decode": "none" }, { "model": "Nemotron 3 Nano Omni 30B", "quant": "", "engine": "vLLM 0.31.0, 128K ctx, 1 user", "tps": 58.8, "spec_decode": "none" }, { "model": "Gemma 4 26B-A4B", "quant": "+ official MTP drafter (k=3)", "engine": "vLLM 0.31.0, 128K ctx, 1 user", "tps": 56.5, "spec_decode": "MTP" }, { "model": "Gemma 4 26B-A4B", "quant": "no speculative decoding", "engine": "vLLM 0.31.0, 128K ctx, 1 user", "tps": 30.8, "spec_decode": "none" }, { "model": "gpt-oss 120B", "quant": "", "engine": "vLLM 0.31.0, 128K ctx, 1 user", "tps": 44.4, "spec_decode": "none" }, { "model": "Qwen3.8 Flash-Next", "quant": "+ MTP (k=2)", "engine": "patched vLLM (qwen38-flash-dgx), 128K ctx, 1 user", "tps": 26.8, "spec_decode": "MTP" }, { "model": "Nemotron 3 Super 120B-A12B", "quant": "+ MTP (k=3)", "engine": "vLLM 0.31.0, 128K ctx, 1 user", "tps": 23.0, "spec_decode": "MTP" }, { "model": "Nemotron 3 Super 120B-A12B", "quant": "no speculative decoding", "engine": "vLLM 0.31.0, 128K ctx, 1 user", "tps": 16.1, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "+ MTP (k=2)", "engine": "vLLM 0.31.0, 128K ctx, 1 user", "tps": 19.0, "spec_decode": "MTP" }, { "model": "Qwen3.8 27B", "quant": "no speculative decoding", "engine": "vLLM 0.31.0, 128K ctx, 1 user", "tps": 11.0, "spec_decode": "none" }, { "model": "Llama 3.1 8B", "quant": "", "engine": "NVIDIA NIM 2.0.9, 128K ctx, 1 user", "tps": 26.4, "spec_decode": "none" } ], "source": "https://ctala.github.io/local-llm-agentic-workflows/benchmarks/", "note": "Cristian Tala, last updated 6 Oct 2026. Single-user decode tok/s: median of 3 streamed requests with a ~200-word prompt; every model loaded with 128K context and memory budgeted for 4 concurrent users. Quant only stated for Qwen3.6 35B-A3B (NVFP4). With 4 users, total throughput was higher (e.g. 240 tok/s for Qwen3.6 35B-A3B + MTP). Eagle3 slowed gpt-oss down (96.2 to 59.4 on 20B), so it is not listed.", "page_url": "https://getrigbase.com/builds/dgx-spark-vllm-23-models-ctala-oct-2026/" }, { "slug": "minisforum-ms-s1-max-ryzen-ai-max-pro-495-192gb", "title": "Minisforum MS-S1 MAX (Ryzen AI Max+ PRO 495, 192 GB): gpt-oss 120B at 55.8 tok/s", "gpus": "AMD Ryzen AI Max+ PRO 495 (Radeon 8065S iGPU, 40 CUs)", "vram_gb": 192, "cpu": "AMD Ryzen AI Max+ PRO 495 (\"Gorgon Halo\")", "motherboard": "", "ram": "192 GB LPDDR5X-8533 unified (GPU limit raised to 170 GB via ttm.pages_limit)", "psu": "", "other": "2 TB SSD, dual 10GbE, Ubuntu Server 26.04.1 (kernel 7.0), llama.cpp Vulkan, flash attention on, q8_0 KV cache, ubatch 2048", "cost": 7399, "currency": "USD", "power": "", "results": [ { "model": "gpt-oss 120B", "quant": "MXFP4", "engine": "llama.cpp Vulkan", "tps": 55.8, "spec_decode": "none" }, { "model": "gpt-oss 120B", "quant": "MXFP4, 32K context", "engine": "llama.cpp Vulkan", "tps": 45.1, "spec_decode": "none" }, { "model": "MiniMax M2.7", "quant": "IQ4_XS", "engine": "llama.cpp Vulkan", "tps": 29.5, "spec_decode": "none" }, { "model": "MiniMax M2.7", "quant": "IQ4_XS, 32K context", "engine": "llama.cpp Vulkan", "tps": 17.3, "spec_decode": "none" }, { "model": "DeepSeek V4 Flash", "quant": "IQ3_S", "engine": "llama.cpp Vulkan", "tps": 17.4, "spec_decode": "none" }, { "model": "DeepSeek V4 Flash", "quant": "IQ3_S, 32K context", "engine": "llama.cpp Vulkan", "tps": 12.2, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "Q8 (author: \"about 8 tok/s\")", "engine": "llama.cpp Vulkan", "tps": 8, "spec_decode": "none" } ], "source": "https://www.hommie.net/posts/ai-box.html", "note": "Jake's Garage, 2026. Cost is the author's pre-order price. One of the first owner-measured Ryzen AI Max+ 495 results. The author found the default f16 KV cache was the long-context bottleneck on Vulkan; q8_0 KV roughly doubled 32K prompt processing. ROCm was slower than tuned Vulkan for generation.", "page_url": "https://getrigbase.com/builds/minisforum-ms-s1-max-ryzen-ai-max-pro-495-192gb/" }, { "slug": "rtx-5070-12gb-qwen38-flash-next-expert-streaming", "title": "RTX 5070 12GB + 32 GB DDR4: Qwen3.8-Flash-Next 177B at 11.5 tok/s with expert streaming", "gpus": "RTX 5070 12GB", "vram_gb": 12, "cpu": "AMD Ryzen 5 5600GT", "motherboard": "", "ram": "32 GB DDR4-2400", "psu": "", "other": "PCIe Gen3, Windows, custom llama.cpp-based expert streaming with a page-locked hot-expert tier", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.8 Flash-Next", "quant": "UD-IQ3_XXS, experts streamed from RAM/SSD", "engine": "llama.cpp-based expert streaming (custom)", "tps": 11.5, "spec_decode": "none" }, { "model": "Qwen3.8 Flash-Next", "quant": "UD-IQ3_XXS, long coding prompt (4,892 tokens out)", "engine": "llama.cpp-based expert streaming (custom)", "tps": 10.15, "spec_decode": "none" } ], "source": "https://github.com/ggml-org/llama.cpp/discussions/29930", "source2": "https://github.com/MohammadHaishemKhawaja/hashyy", "note": "llama.cpp Show and tell discussion, 4 Oct 2026. Benchmark ~11.5 tok/s (up from ~7 on the inherited setup); the author saw 14-15 tok/s in normal conversations. Most of the model lives off the GPU (offload), so speed depends heavily on RAM/SSD I/O.", "page_url": "https://getrigbase.com/builds/rtx-5070-12gb-qwen38-flash-next-expert-streaming/" }, { "slug": "epyc-7532-rtx-pro-6000-rtx-3090-ti-qwen38-flash-next", "title": "EPYC 7532 workstation (RTX Pro 6000 + RTX 3090 Ti, 300 W caps): Qwen3.8-Flash-Next, llama.cpp vs Strata engine", "gpus": "RTX Pro 6000 Blackwell Workstation (96 GB) + RTX 3090 Ti (EVGA FTW3 Ultra, 24 GB); each test used one card", "vram_gb": 120, "cpu": "AMD EPYC 7532 (32 cores, Zen 2)", "motherboard": "", "ram": "128 GB DDR4-3200, 8 channels", "psu": "", "other": "Both cards power-capped to 300 W for the bench; one request at a time, 4,038-token prompt, 256 tokens out, greedy", "cost": null, "currency": "", "power": "Board power (not wall), 4K prompt: RTX Pro 6000 297.8 W (llama.cpp) / 290.6 W (Strata); RTX 3090 Ti 168.5 W (llama.cpp) / 281.1 W (Strata)", "results": [ { "model": "Qwen3.8 Flash-Next", "quant": "IQ3_S, all experts on the RTX Pro 6000, no draft head", "engine": "llama.cpp (3cf03257), RTX Pro 6000", "tps": 102.889, "spec_decode": "speculative" }, { "model": "Qwen3.8 Flash-Next", "quant": "IQ3_S + model draft head, all experts on the RTX Pro 6000", "engine": "Strata engine 0.1.39, RTX Pro 6000", "tps": 205.263, "spec_decode": "speculative" }, { "model": "Qwen3.8 Flash-Next", "quant": "IQ3_S, 32K prompt, all on the RTX Pro 6000", "engine": "llama.cpp (3cf03257), RTX Pro 6000", "tps": 76.337, "spec_decode": "none" }, { "model": "Qwen3.8 Flash-Next", "quant": "IQ3_S, experts of 34/48 layers in system RAM, no draft head", "engine": "llama.cpp (b11514), RTX 3090 Ti", "tps": 22.657, "spec_decode": "speculative" }, { "model": "Qwen3.8 Flash-Next", "quant": "IQ3_S + draft head, 8,144 of 24,576 experts on card", "engine": "Strata engine 0.1.39, RTX 3090 Ti", "tps": 98.291, "spec_decode": "speculative" } ], "source": "https://research.strata2signal.com/flash-next-on-three-cards/", "note": "strata→signal research, published 12 Oct 2026; runs 6-9 Oct 2026. Median of three runs. llama.cpp ran near defaults (512-token micro-batch, 8 threads); on the 3090 Ti its speed was limited by those 8 CPU threads, not the card. Strata is a third-party engine built on llama.cpp (github.com/Niko1221/Strata). The source also benches an RTX 5090 laptop GPU (24.4 tok/s llama.cpp, 85.6 Strata).", "page_url": "https://getrigbase.com/builds/epyc-7532-rtx-pro-6000-rtx-3090-ti-qwen38-flash-next/" }, { "slug": "llmspeed-rtx-5090-oct-2026", "title": "RTX 5090 (llm-speed signed runs, Oct 2026): gpt-oss 20B 324 tok/s, Qwen3.8 27B 75 tok/s", "gpus": "RTX 5090", "vram_gb": 32, "cpu": "AMD EPYC 7543 (Qwen3.8 run: EPYC 9534)", "motherboard": "", "ram": "", "psu": "", "other": "Server hosts; llm-speed suite-v1 chat-short workload (128-token prompt, 256 tokens out, batch 1)", "cost": null, "currency": "", "power": "", "results": [ { "model": "gpt-oss 20B", "quant": "reported as Q4_K_M", "engine": "llama.cpp 0.7.0-dev", "tps": 324.2, "spec_decode": "none" }, { "model": "Qwen3.6 27B", "quant": "Q4_K_M", "engine": "llama.cpp 0.7.0-dev", "tps": 75.1, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "Q4_K_M (EPYC 9534 host)", "engine": "llama.cpp 0.7.0-dev (b11669)", "tps": 75.2, "spec_decode": "none" } ], "source": "https://llm-speed.com/r/r_dwp5fgdapmw", "source2": "https://llm-speed.com/r/r_pob-co51bd-", "source3": "https://llm-speed.com/r/r_qfrb2zmyk0k", "note": "Signed community runs on llm-speed.com, 15-17 Oct 2026. Decode tok/s from the chat-short workload; the chat-long (4K prompt) runs were 311.0 (gpt-oss 20B), 74.5 (Qwen3.6 27B) and 74.6 (Qwen3.8 27B). Signing records provenance, not independent hardware verification.", "page_url": "https://getrigbase.com/builds/llmspeed-rtx-5090-oct-2026/" }, { "slug": "llmspeed-rtx-4090-oct-2026", "title": "RTX 4090 (llm-speed signed runs, Oct 2026): Qwen3.6 35B-A3B 161 tok/s, Gemma 4 12B QAT 98 tok/s", "gpus": "RTX 4090", "vram_gb": 24, "cpu": "AMD EPYC 7642 (GLM run: EPYC 75F3)", "motherboard": "", "ram": "", "psu": "", "other": "Server hosts; llm-speed suite-v1 chat-short workload (128-token prompt, 256 tokens out, batch 1)", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "Q4_K_M", "engine": "llama.cpp b8931", "tps": 160.6, "spec_decode": "none" }, { "model": "Gemma 4 12B", "quant": "QAT Q4_0", "engine": "llama.cpp b8931", "tps": 97.7, "spec_decode": "none" }, { "model": "GLM 4.7 Flash", "quant": "Q4_K_M (EPYC 75F3 host)", "engine": "Ollama 0.31.1", "tps": 164.9, "spec_decode": "none" } ], "source": "https://llm-speed.com/r/r_m6j1zxwvp-f", "source2": "https://llm-speed.com/r/r_jfh0qkngw76", "source3": "https://llm-speed.com/r/r_ted0u8uyre0", "note": "Signed community runs on llm-speed.com, 16 Oct 2026. Decode tok/s from the chat-short workload; chat-long (4K prompt): 160.4 (Qwen3.6 35B-A3B), 86.3 (Gemma 4 12B), 146.1 (GLM 4.7 Flash).", "page_url": "https://getrigbase.com/builds/llmspeed-rtx-4090-oct-2026/" }, { "slug": "llmspeed-rtx-3090-oct-2026", "title": "RTX 3090 (llm-speed signed runs, Oct 2026): gpt-oss 20B 207 tok/s, Qwen3.6 27B 43 tok/s", "gpus": "RTX 3090", "vram_gb": 24, "cpu": "AMD EPYC 7282 (GLM run: Threadripper PRO 5975WX)", "motherboard": "", "ram": "", "psu": "", "other": "Server hosts; llm-speed suite-v1 chat-short workload (128-token prompt, 256 tokens out, batch 1)", "cost": null, "currency": "", "power": "", "results": [ { "model": "gpt-oss 20B", "quant": "reported as Q4_K_M", "engine": "llama.cpp 0.7.0-dev", "tps": 207.2, "spec_decode": "none" }, { "model": "Qwen3.6 27B", "quant": "Q4_K_M", "engine": "llama.cpp 0.7.0-dev", "tps": 42.7, "spec_decode": "none" }, { "model": "GLM 4.7 Flash", "quant": "Q4_K_M (Threadripper PRO host)", "engine": "Ollama 0.31.1", "tps": 134.6, "spec_decode": "none" } ], "source": "https://llm-speed.com/r/r_nakw1nq6clw", "source2": "https://llm-speed.com/r/r_qm1xyvjmx_r", "source3": "https://llm-speed.com/r/r_kalwcyab-86", "note": "Signed community runs on llm-speed.com, 16-17 Oct 2026. Decode tok/s from the chat-short workload; chat-long (4K prompt): 199.8 (gpt-oss 20B), 42.3 (Qwen3.6 27B), 116.1 (GLM 4.7 Flash).", "page_url": "https://getrigbase.com/builds/llmspeed-rtx-3090-oct-2026/" }, { "slug": "llmspeed-2x-rtx-3090-llama33-70b-oct-2026", "title": "2x RTX 3090 (llm-speed signed run, Oct 2026): Llama 3.3 70B Q4_K_M at 19.1 tok/s", "gpus": "2x RTX 3090", "vram_gb": 48, "cpu": "AMD EPYC 7H12 (64 cores)", "motherboard": "", "ram": "", "psu": "", "other": "Server host; llm-speed suite-v1 chat-short workload (128-token prompt, 256 tokens out, batch 1)", "cost": null, "currency": "", "power": "", "results": [ { "model": "Llama 3.3 70B", "quant": "Q4_K_M", "engine": "llama.cpp 0.7.0-dev", "tps": 19.1, "spec_decode": "none" } ], "source": "https://llm-speed.com/r/r_n93ukk0mkmm", "note": "Signed community run on llm-speed.com, 17 Oct 2026. chat-long (4K prompt) decode: 18.7 tok/s.", "page_url": "https://getrigbase.com/builds/llmspeed-2x-rtx-3090-llama33-70b-oct-2026/" }, { "slug": "minisforum-ms-s1-max-strix-halo-qwen36-35b-vitorpontual", "title": "Minisforum MS-S1 MAX (Strix Halo, 128 GB): Qwen3.6 35B-A3B at 62 tok/s on llama.cpp Vulkan", "gpus": "AMD Ryzen AI Max+ 395 (Radeon 8060S iGPU)", "vram_gb": 128, "cpu": "AMD Ryzen AI Max+ 395", "motherboard": "", "ram": "128 GB unified", "psu": "", "other": "Minisforum MS-S1 MAX mini PC; Ubuntu 26.04, kernel 7.0, Mesa RADV (Vulkan 1.4.335), ROCm 10.1.0, llama.cpp commit d7a695e", "cost": 3799, "currency": "USD", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "Q4_K_M (Unsloth)", "engine": "llama.cpp Vulkan", "tps": 62, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "Q8_0", "engine": "llama.cpp Vulkan", "tps": 54, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "Q4_K_M (Unsloth)", "engine": "llama.cpp ROCm", "tps": 51, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "Q4_K_M (Unsloth)", "engine": "llama.cpp Vulkan + MTP", "tps": 93, "spec_decode": "MTP" } ], "source": "https://vitorpontual.com/posts/two-ai-boxes-3-the-benchmarks/", "note": "Measured 6 Oct 2026 (thinking-mode workload, medians). MTP figure is from the 7 Oct update (code/JSON output, short check). llama-bench tg128 at depth 0: Vulkan Q4 63, Q8 54, ROCm Q4 54, ROCm Q8 48; at 65K depth Vulkan Q4 43. Cost is what the author says he paid.", "page_url": "https://getrigbase.com/builds/minisforum-ms-s1-max-strix-halo-qwen36-35b-vitorpontual/" }, { "slug": "dgx-spark-qwen36-35b-nvfp4-mtp-vitorpontual", "title": "DGX Spark (GB10): Qwen3.6 35B-A3B NVFP4 at 113 tok/s on vLLM with MTP", "gpus": "Nvidia GB10 (unified)", "vram_gb": 128, "cpu": "Nvidia GB10 (20 Arm cores)", "motherboard": "", "ram": "128 GB unified", "psu": "", "other": "NVIDIA DGX Spark; vLLM 0.30, NVFP4 weights, MTP speculative decoding", "cost": 3999, "currency": "USD", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "NVFP4", "engine": "vLLM 0.30 + MTP", "tps": 113, "spec_decode": "MTP" }, { "model": "Qwen3.6 35B-A3B", "quant": "NVFP4", "engine": "vLLM 0.30 + MTP", "tps": 86, "spec_decode": "MTP" } ], "source": "https://vitorpontual.com/posts/two-ai-boxes-3-the-benchmarks/", "note": "Measured 6 Oct 2026, median of 4 runs. 113 tok/s = thinking-mode workload; 86 = multi-step assistant session; 82 on short structured JSON. Prefill: 100K-token cold prompt in about 30 s. 16 concurrent requests: 311 tok/s total (23 per request). Cost: author says he paid about $3,999 at launch.", "page_url": "https://getrigbase.com/builds/dgx-spark-qwen36-35b-nvfp4-mtp-vitorpontual/" }, { "slug": "strix-halo-r9700-oculink-strixpro-qwen38-flash-next", "title": "Strix Halo + Radeon AI Pro R9700 over OCuLink: Qwen3.8 Flash-Next at ~102 tok/s (custom engine)", "gpus": "AMD Radeon AI Pro R9700 32 GB (OCuLink x4) + Ryzen AI Max+ 395 Radeon 8060S iGPU", "vram_gb": 160, "cpu": "AMD Ryzen AI Max+ (Strix Halo) mini PC", "motherboard": "", "ram": "128 GB unified (Strix Halo)", "psu": "", "other": "Lian Li B4 case; R9700 power-capped to 210 W, -500 MHz, fan capped at 1500 rpm; iGPU capped at 2000 MHz, CPU boost off", "cost": null, "currency": "", "power": "R9700 capped at 210 W", "results": [ { "model": "Qwen3.8 Flash-Next", "quant": "custom int4 experts / 6-bit trunk", "engine": "strixpro (MTP depth 3 + prompt lookup)", "tps": 102, "spec_decode": "MTP" }, { "model": "Qwen3.8 Flash-Next", "quant": "custom int4 experts / 6-bit trunk", "engine": "strixpro, ~100K context", "tps": 82, "spec_decode": "none" }, { "model": "Qwen3.8 Flash-Next", "quant": "custom int4 experts / 6-bit trunk", "engine": "strixpro, ~980K context", "tps": 71, "spec_decode": "none" }, { "model": "Qwen3.8 Flash-Next", "quant": "GGUF", "engine": "llama.cpp (Strix Halo iGPU only)", "tps": "29-34", "spec_decode": "none" } ], "source": "https://vuln.dev/flash-engine/", "note": "Blog post 5 Oct 2026, updated 16 Oct 2026. Hot experts and trunk run on the R9700, all experts held on the Strix Halo. Prefill about 2,000 tok/s on 4-16K prompts. \"~102\" is the author's approximate figure for short prompts.", "page_url": "https://getrigbase.com/builds/strix-halo-r9700-oculink-strixpro-qwen38-flash-next/" }, { "slug": "mac-studio-m5-max-36gb-rapid-mlx-oct-2026", "title": "Mac Studio M5 Max 36 GB: Qwen3.6 35B-A3B at 173 tok/s (Rapid-MLX), 146 on oMLX", "gpus": "Apple M5 Max (unified)", "vram_gb": 36, "cpu": "Apple M5 Max", "motherboard": "", "ram": "36 GB unified", "psu": "", "other": "Mac Studio; Rapid-MLX 0.16.1, oMLX 0.7.0, LM Studio llmster 0.0.25 (MLX runtime 1.11.0); 4-bit MLX weights", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "4-bit MLX", "engine": "Rapid-MLX 0.16.1 (MTP)", "tps": 173.4, "spec_decode": "MTP" }, { "model": "Qwen3.6 35B-A3B", "quant": "4-bit MLX", "engine": "oMLX 0.7.0", "tps": 145.8, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "4-bit MLX", "engine": "LM Studio (MLX)", "tps": 124.5, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "4-bit MLX", "engine": "Rapid-MLX 0.16.1 (MTP)", "tps": 37.9, "spec_decode": "MTP" }, { "model": "Qwen3.8 27B", "quant": "4-bit MLX", "engine": "oMLX 0.7.0", "tps": 25.3, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "4-bit MLX", "engine": "LM Studio (MLX)", "tps": 26.0, "spec_decode": "none" }, { "model": "Qwen3.5 9B", "quant": "4-bit MLX", "engine": "Rapid-MLX 0.16.1 (MTP)", "tps": 115.5, "spec_decode": "MTP" }, { "model": "Qwen3.5 9B", "quant": "4-bit MLX", "engine": "oMLX 0.7.0", "tps": 78.0, "spec_decode": "none" } ], "source": "https://rapidmlx.com/compare", "note": "Measured 12 Oct 2026, single-stream decode, single run per engine. Published by the Rapid-MLX developers (vendor benchmark). 4-stream aggregate: Qwen3.6 35B-A3B 293.9 tok/s (Rapid-MLX), Qwen3.8 27B 82.8 tok/s.", "page_url": "https://getrigbase.com/builds/mac-studio-m5-max-36gb-rapid-mlx-oct-2026/" }, { "slug": "macbook-pro-m5-max-128gb-moe-servers-baobab", "title": "MacBook Pro M5 Max 128 GB: Qwen3.6 35B-A3B at 271 tok/s (mlx-serve + MTP), Gemma 4 26B-A4B at 166", "gpus": "Apple M5 Max (unified)", "vram_gb": 128, "cpu": "Apple M5 Max", "motherboard": "", "ram": "128 GB unified", "psu": "", "other": "MacBook Pro on AC power, macOS 26.6; mlx_lm.server 0.32.2, Rapid-MLX 0.16.1, mlx-serve 26.10.2; 4-bit MLX weights", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "4-bit MLX (mlx-serve build)", "engine": "mlx-serve 26.10.2 + MTP", "tps": 271, "spec_decode": "MTP" }, { "model": "Qwen3.6 35B-A3B", "quant": "4-bit MLX", "engine": "Rapid-MLX 0.16.1 + MTP", "tps": 240, "spec_decode": "MTP" }, { "model": "Qwen3.6 35B-A3B", "quant": "4-bit MLX", "engine": "mlx_lm.server 0.32.2", "tps": 107, "spec_decode": "none" }, { "model": "Gemma 4 26B-A4B", "quant": "4-bit MLX (QAT)", "engine": "Rapid-MLX 0.16.1 + DFlash", "tps": 166, "spec_decode": "DFlash" }, { "model": "Qwen3.8 27B", "quant": "4-bit MLX", "engine": "same server, no speculation", "tps": 31, "spec_decode": "none" } ], "source": "https://baobabtech.ai/posts/local-moe-m5-max-qwen-gemma-speed", "note": "Measured 19 Oct 2026, temperature 0, one run per setup (repeats varied up to 15%). Qwen3.6 figures are on the coding prompt. Qwen3.8 27B ran about 12 tok/s on battery vs about 31 on AC. 4 concurrent requests: 412 tok/s total on mlx-serve + MTP.", "page_url": "https://getrigbase.com/builds/macbook-pro-m5-max-128gb-moe-servers-baobab/" }, { "slug": "hp-zbook-ultra-g1a-strix-halo-laptop-petronella", "title": "HP ZBook Ultra G1a (Strix Halo laptop, 128 GB): Qwen3.6 35B-A3B at 59.4 tok/s, gpt-oss 120B at 44.7", "gpus": "AMD Ryzen AI Max+ PRO 395 (Radeon 8060S iGPU, 32 GB dedicated)", "vram_gb": 128, "cpu": "AMD Ryzen AI Max+ PRO 395", "motherboard": "", "ram": "128 GB unified", "psu": "", "other": "Laptop on Windows 11, HP Optimized power plan, plugged in; llama.cpp b11213 (Vulkan and ROCm 7.13), Ollama 0.34.0", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 35B-A3B", "quant": "Q4_K_M", "engine": "llama.cpp b11213 Vulkan", "tps": 59.4, "spec_decode": "none" }, { "model": "gpt-oss 120B", "quant": "MXFP4", "engine": "llama.cpp b11213 Vulkan", "tps": 44.7, "spec_decode": "none" }, { "model": "Qwen3.6 35B-A3B", "quant": "4-bit", "engine": "Ollama 0.34.0", "tps": 54.4, "spec_decode": "none" }, { "model": "Gemma 4 26B-A4B", "quant": "4-bit", "engine": "Ollama 0.34.0", "tps": 51.8, "spec_decode": "none" }, { "model": "Qwen3.6 27B", "quant": "4-bit", "engine": "Ollama 0.34.0", "tps": 11.4, "spec_decode": "none" }, { "model": "Gemma 4 31B", "quant": "4-bit", "engine": "Ollama 0.34.0", "tps": 8.8, "spec_decode": "none" } ], "source": "https://petronellatech.com/blog/dgx-spark-vs-strix-halo-vs-gorgon-halo-local-llm-benchmarks/", "note": "Measured 27 Sep 2026 by Petronella Technology Group. llama-bench tg128, empty context, mean of 3. Same-day DGX Spark with the same build: 69.5 (Qwen3.6 35B-A3B) and 59.5 (gpt-oss 120B). gpt-oss 120B at 32K context collapsed to 4.7 tok/s on Vulkan but held 32.3 on ROCm.", "page_url": "https://getrigbase.com/builds/hp-zbook-ultra-g1a-strix-halo-laptop-petronella/" }, { "slug": "llmspeed-rtx-4090-flint-40b-a2-5b-oct-2026", "title": "RTX 4090 (llm-speed signed run, Oct 2026): Flint 40B-A2.5B at 169-179 tok/s on llama.cpp", "gpus": "RTX 4090", "vram_gb": 24, "cpu": "AMD EPYC 7452 (32 cores)", "motherboard": "", "ram": "504 GB", "psu": "", "other": "Server host; llm-speed suite-v1 chat-short workload", "cost": null, "currency": "", "power": "", "results": [ { "model": "Flint 40B-A2.5B", "quant": "NVFP4", "engine": "llama.cpp 0.7.0-dev (build 11803)", "tps": 179.4, "spec_decode": "none" }, { "model": "Flint 40B-A2.5B", "quant": "UD-Q3_K_XL", "engine": "llama.cpp 0.7.0-dev (build 11803)", "tps": 169.2, "spec_decode": "none" } ], "source": "https://llm-speed.com/r/r_sw92j6kg7js", "note": "Signed community runs on llm-speed.com, 22 Oct 2026. NVFP4 run: https://llm-speed.com/r/r_0i0a9uq6fdx (chat-long 167.2 tok/s). Q3_K_XL run (16K context) chat-long: 157.8 tok/s.", "page_url": "https://getrigbase.com/builds/llmspeed-rtx-4090-flint-40b-a2-5b-oct-2026/" }, { "slug": "llmspeed-rtx-3090-flint-40b-a2-5b-oct-2026", "title": "RTX 3090 (llm-speed signed run, Oct 2026): Flint 40B-A2.5B Q3_K_XL at 128.9 tok/s", "gpus": "RTX 3090", "vram_gb": 24, "cpu": "AMD EPYC 7763 (64 cores)", "motherboard": "", "ram": "1008 GB", "psu": "", "other": "Server host; llm-speed suite-v1 chat-short workload", "cost": null, "currency": "", "power": "", "results": [ { "model": "Flint 40B-A2.5B", "quant": "UD-Q3_K_XL", "engine": "llama.cpp 0.7.0-dev (build 11803)", "tps": 128.9, "spec_decode": "none" } ], "source": "https://llm-speed.com/r/r_v-sdiljpm7i", "note": "Signed community run on llm-speed.com, 22 Oct 2026, 16K context. chat-long (4K prompt) decode: 123.0 tok/s. An 8K-context run on the same host: 130.8 tok/s (https://llm-speed.com/r/r_-yeg4qnhjp7).", "page_url": "https://getrigbase.com/builds/llmspeed-rtx-3090-flint-40b-a2-5b-oct-2026/" }, { "slug": "i7-12700k-rtx-5060-ti-rtx-3060-qwen38-27b-jesvs", "title": "i7-12700K + RTX 5060 Ti 16GB + RTX 3060 12GB: Qwen3.8 27B from 21 to 51 tok/s (split, MTP, single-GPU)", "gpus": "RTX 5060 Ti 16GB + RTX 3060 12GB", "vram_gb": 28, "cpu": "Intel Core i7-12700K", "motherboard": "", "ram": "64 GB", "psu": "", "other": "Mismatched pair: 5060 Ti on PCIe 5.0, 3060 negotiating PCIe 3.0 x4; llama.cpp layer split -ts 5/3 (enabling P2P changed nothing)", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.8 27B", "quant": "Q4_K_M, layer split 5/3 across both GPUs", "engine": "llama.cpp (May 2026 build)", "tps": 21.2, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "Q4_K_M, split + MTP draft (n_max=2)", "engine": "llama.cpp master (Oct 2026)", "tps": 37.6, "spec_decode": "MTP" }, { "model": "Qwen3.8 27B", "quant": "UD-IQ4_XS on the 5060 Ti alone, 8K ctx, q8 KV", "engine": "llama.cpp master (Oct 2026)", "tps": 28.0, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "UD-IQ4_XS on the 5060 Ti + MTP draft on the 3060 (n_max=2)", "engine": "llama.cpp master (Oct 2026)", "tps": 51.4, "spec_decode": "MTP" } ], "source": "https://jesvs.com/posts/llama-cpp-dual-gpu-optimization/", "note": "JESVS blog, 6 Oct 2026. Prefill 1,144 tok/s at the 5/3 split; row split mode dropped prefill to 228 tok/s. On code-edit prompts MTP reached 46.9 tok/s (split) and 58.1 tok/s (single-GPU sprint config, 8K context). A 35B-A3B MoE distill on the same pair went 111.8 -> 118.6 tok/s on llama.cpp master (not listed: exact model not named).", "page_url": "https://getrigbase.com/builds/i7-12700k-rtx-5060-ti-rtx-3060-qwen38-27b-jesvs/" }, { "slug": "ryzen-9950x3d-rtx-5090-128gb-four-big-moes-gadeyne", "title": "Ryzen 9 9950X3D + RTX 5090 + 128 GB DDR5: four big MoEs with experts offloaded to RAM (Qwen3.8 Flash-Next 78.8 tok/s)", "gpus": "RTX 5090", "vram_gb": 32, "cpu": "AMD Ryzen 9 9950X3D (16 cores)", "motherboard": "", "ram": "128 GB DDR5-5600 (4x32 GB)", "psu": "", "other": "Arch Linux, NVIDIA 615.78, CUDA 13.4; GPU also drives the desktop (3 GiB kept free); runtimes keep hot experts in VRAM and the rest in system RAM", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.8 Flash-Next", "quant": "Unsloth UD-Q5_K_XL, MTP drafting, experts offloaded to RAM", "engine": "Strata (296b59d)", "tps": 78.8, "spec_decode": "MTP" }, { "model": "Ornith 1.5 122B", "quant": "EXL3 4 bpw, 180/256 experts per layer in RAM, no drafting", "engine": "TabbyAPI + ExLlamaV3 1.6.2", "tps": 46.4, "spec_decode": "none" }, { "model": "GLM-5.3 Flash", "quant": "EXL3 3.05 bpw, MTP drafting, experts offloaded to RAM", "engine": "Project Maya 1.1.21", "tps": 30.1, "spec_decode": "MTP" }, { "model": "DeepSeek V4.1 Flash", "quant": "Maya-S (experts 2-3 bit), partly streamed from SSD", "engine": "Project Maya 1.1.21 (maya-ds4)", "tps": 24.9, "spec_decode": "none" } ], "source": "https://dev.to/seppegadeyne/four-big-local-llms-on-one-rtx-5090-and-128-gb-of-ram-benchmarked-side-by-side-2h8j", "note": "Seppe Gadeyne (dev.to), updated measurements 22 Oct 2026; median of 8 runs (Dutch prose + coding prompt), greedy, 512 output tokens. All four are MoEs with CPU offload of experts to system RAM: Qwen3.8 Flash-Next, Ornith, GLM-5.3 Flash and DeepSeek V4.1 Flash. Prefill at ~125K prompt: 4,252 / 1,508 / 2,445 / 2,570 tok/s in the same order. Versions: Strata 296b59d, Project Maya 1.1.21, TabbyAPI d6e6227 + ExLlamaV3 1.6.2.", "page_url": "https://getrigbase.com/builds/ryzen-9950x3d-rtx-5090-128gb-four-big-moes-gadeyne/" }, { "slug": "rtx-5090-strata-qwen38-flash-next-akitaonrails", "title": "RTX 5090 workstation: Qwen3.8 Flash-Next IQ3_S at 159.2 tok/s on Strata (expert offload)", "gpus": "RTX 5090", "vram_gb": 32, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "Strata expert-offload engine: hot experts on the GPU, all experts in system RAM, ~29 GB lookup table on SSD; GPU also drives the Hyprland desktop (4 GiB reserve)", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.8 Flash-Next", "quant": "IQ3_S (~55 GB), experts offloaded to RAM, speculation helper", "engine": "Strata", "tps": 159.2, "spec_decode": "none" } ], "source": "https://akitaonrails.github.io/en/2026/10/22/llm-benchmark-v4-consumer-open-source-rtx5090-strix-halo-strata/", "note": "AkitaOnRails LLM Benchmark v4, 22 Oct 2026. Qwen3.8 Flash-Next runs with CPU offload of experts (Strata). Scored 91.0 (Tier A) on the author's code-review benchmark. With the 4 GiB desktop reserve, throughput in the initial measurement went from ~200 to ~150 tok/s. Ornith-1.5-35B-A3B Q8_0 on the same card/engine: ~80 tok/s.", "page_url": "https://getrigbase.com/builds/rtx-5090-strata-qwen38-flash-next-akitaonrails/" }, { "slug": "strix-halo-128gb-qwen38-27b-akitaonrails", "title": "Strix Halo mini PC (Ryzen AI Max+ 395, 128 GB): Qwen3.8 27B at 12.6 tok/s Q4_K_M, 7.7 Q8_0", "gpus": "AMD Ryzen AI Max+ 395 (Radeon 8060S iGPU)", "vram_gb": 128, "cpu": "AMD Ryzen AI Max+ 395", "motherboard": "", "ram": "128 GB unified", "psu": "", "other": "ROCm (HSA_OVERRIDE_GFX_VERSION, GGML_HIP_NO_VMM, -dio)", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.8 27B", "quant": "Q4_K_M", "engine": "llama.cpp/Ollama, ROCm", "tps": 12.6, "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "Q8_0, --reasoning off", "engine": "llama.cpp/Ollama, ROCm", "tps": 7.7, "spec_decode": "none" } ], "source": "https://akitaonrails.github.io/en/2026/10/22/llm-benchmark-v4-consumer-open-source-rtx5090-strix-halo-strata/", "note": "AkitaOnRails LLM Benchmark v4, 22 Oct 2026. Quoted as the Strix ROCm setup results (HSA_OVERRIDE_GFX_VERSION, GGML_HIP_NO_VMM, -dio); the author notes ~256 GB/s bandwidth makes it too slow for interactive use. The Qwen3.8 27B benchmark run (started on an RTX 5090 at Q5_K_M, resumed on the Strix at Q8_0) scored 80.0 but took 706 minutes.", "page_url": "https://getrigbase.com/builds/strix-halo-128gb-qwen38-27b-akitaonrails/" }, { "slug": "macbook-pro-m4-pro-48gb-flint-40b-kapitsa-rec-llamacpp", "title": "MacBook Pro M4 Pro 48 GB: Flint 40B-A2.5B 4-bit at 127.9 tok/s (Kapitsa), 126.6 (rec), 79.6 (llama.cpp)", "gpus": "Apple M4 Pro (20-core GPU, unified)", "vram_gb": 48, "cpu": "Apple M4 Pro", "motherboard": "", "ram": "48 GB unified", "psu": "", "other": "MacBook Pro 16 (2024, Mac16,7), macOS 27.0.1, AC power, High Power mode; 32K context, 1 slot, T=1.0", "cost": null, "currency": "", "power": "", "results": [ { "model": "Flint 40B-A2.5B", "quant": "int4 MLX (RecursiveSI/flint-int4)", "engine": "Kapitsa (PR #286)", "tps": 127.9, "spec_decode": "none" }, { "model": "Flint 40B-A2.5B", "quant": "int4 MLX (same files)", "engine": "rec 0.1.0 (Recursive)", "tps": 126.56, "spec_decode": "none" }, { "model": "Flint 40B-A2.5B", "quant": "Q4_1 GGUF (RecursiveSI/flint-int4-gguf)", "engine": "llama.cpp b11804, all layers on GPU, -fa on", "tps": 79.57, "spec_decode": "none" } ], "source": "https://github.com/nace-ai/kapitsa/blob/main/bench/results/2026-10-22-flint-m4pro/README.md", "note": "Kapitsa bench results, 22 Oct 2026. Decode median of 5 runs of 512 tokens. Peak memory: Kapitsa 23.3 GB, llama.cpp 26.9, rec 27.9. At 128K context decode falls to 45.7 (Kapitsa), 35.2 (rec), 23.8 (llama.cpp). Some runs flagged for browser GPU contention (2-12%).", "page_url": "https://getrigbase.com/builds/macbook-pro-m4-pro-48gb-flint-40b-kapitsa-rec-llamacpp/" }, { "slug": "llmspeed-rtx-a4000-muse-glimmer-30b-oct-2026", "title": "RTX A4000 16GB (llm-speed signed run, Oct 2026): Muse Glimmer 30B UD-IQ3_XXS at 21.4 tok/s", "gpus": "RTX A4000", "vram_gb": 16, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "llm-speed suite-v1 chat-short workload, 16K context", "cost": null, "currency": "", "power": "", "results": [ { "model": "Muse Glimmer 30B", "quant": "UD-IQ3_XXS", "engine": "llama.cpp 0.7.0-dev (build 11803)", "tps": 21.42, "spec_decode": "none" } ], "source": "https://llm-speed.com/r/r_y9y09s77lfa", "note": "Signed run on llm-speed.com, 22 Oct 2026. chat-long decode 20.82 tok/s (prefill 687 tok/s); agent-trace 20.08 tok/s. For comparison, an RTX 4090 ran UD-Q4_K_XL at 53.70 tok/s chat-short (https://llm-speed.com/r/r_21z5pn8262x).", "page_url": "https://getrigbase.com/builds/llmspeed-rtx-a4000-muse-glimmer-30b-oct-2026/" }, { "slug": "capy-flash-2x-rtx-5070-ti-ryzen-9900x", "title": "Dual RTX 5070 Ti on Ryzen 9 9900X (capy-flash maintainer rig)", "gpus": "2x RTX 5070 Ti 16 GB", "vram_gb": 32, "cpu": "AMD Ryzen 9 9900X (12 cores)", "motherboard": "", "ram": "96 GB DDR5-5600", "psu": "", "other": "PCIe 5.0 x8+x8, no P2P; Ubuntu, CUDA 13.1", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.8 Flash-Next", "quant": "GSQ-RCO (64 GB edition)", "engine": "capy-flash v0.7.0, 2 GPUs, agentic decode", "tps": 182, "how": "Maintainer/user decode tok/s from the capy-flash README", "spec_decode": "none" }, { "model": "Qwen3.8 Flash-Next", "quant": "NVFP4", "engine": "capy-flash v0.6.0, 2 GPUs, agentic decode", "tps": 155, "how": "Maintainer/user decode tok/s from the capy-flash README", "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "3-bit GSQ", "engine": "capy-flash (1 GPU), MTP, decode at 8k prompt", "tps": 139, "how": "Maintainer/user decode tok/s from the capy-flash README", "spec_decode": "MTP", "context": "8k prompt" }, { "model": "Qwen3.8 27B", "quant": "3-bit GSQ", "engine": "capy-flash, 2 GPUs, MTP, decode at 8k prompt", "tps": 230, "how": "Maintainer/user decode tok/s from the capy-flash README", "spec_decode": "MTP", "context": "8k prompt" } ], "source": "https://github.com/ValerioDolci/capy-flash", "note": "Benchmarks section of the capy-flash README, measured by the maintainer on his own machine. The README also lists LocalMaxxing reasoning-v1 prompt figures (234 tok/s Flash-Next 64 GB, 288 tok/s 27B on 2 GPUs), which are higher than the decode figures used here. Found via localmaxxing (discovery only); numbers re-checked at the original source on 2026-10-24.", "page_url": "https://getrigbase.com/builds/capy-flash-2x-rtx-5070-ti-ryzen-9900x/" }, { "slug": "capy-flash-2x-rtx-5080-epyc-7f52", "title": "Dual RTX 5080 on EPYC 7F52 (capy-flash user report)", "gpus": "2x RTX 5080 16 GB", "vram_gb": 32, "cpu": "AMD EPYC 7F52 (16-core Zen 2)", "motherboard": "", "ram": "256 GB DDR4-3200 ECC (8x32 GB)", "psu": "", "other": "PCIe 4.0 x16 each, P2P-patched driver; WD_BLACK SN8100 1 TB", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.8 Flash-Next", "quant": "NVFP4", "engine": "capy-flash v0.7.2 (CUDA 13.4), agentic decode", "tps": 141.3, "how": "User-reported (issue #11), listed in the capy-flash README", "spec_decode": "none" }, { "model": "Qwen3.8 Flash-Next", "quant": "NVFP4", "engine": "capy-flash v0.7.2, --expert-cpu-threads 15, agentic decode", "tps": 144.4, "how": "User-reported (issue #11), listed in the capy-flash README", "spec_decode": "none" } ], "source": "https://github.com/ValerioDolci/capy-flash/issues/11", "source2": "https://github.com/ValerioDolci/capy-flash", "note": "Hardware from user Hotcooler's issue #11 (2026-10-19); speeds from the 'Reported by users' table in the capy-flash README. On a Strata NVFP4 fork the same user reports ~145 tok/s agentic, 110-130 prose. Found via localmaxxing (discovery only); numbers re-checked at the original source on 2026-10-24.", "page_url": "https://getrigbase.com/builds/capy-flash-2x-rtx-5080-epyc-7f52/" }, { "slug": "capy-flash-2x-rtx-pro-4000-threadripper-5975wx", "title": "Dual RTX PRO 4000 Blackwell on Threadripper PRO 5975WX", "gpus": "2x RTX PRO 4000 Blackwell 24 GB", "vram_gb": 48, "cpu": "AMD Threadripper PRO 5975WX", "motherboard": "", "ram": "", "psu": "", "other": "PCIe 4.0 x16; Proxmox LXC", "cost": null, "currency": "", "power": "145 W per card", "results": [ { "model": "Qwen3.8 Flash-Next", "quant": "GSQ-RCO (64 GB edition)", "engine": "capy-flash v0.7.1, prose decode (low end of 100-114)", "tps": 100, "how": "User-reported (issue #22); low end of the stated prose range", "prefill_tps": 3300, "prefill_note": "~3,300 tok/s on a 40k-token prompt (user report)", "spec_decode": "none" } ], "source": "https://github.com/ValerioDolci/capy-flash/issues/22", "note": "User jikdoc, issue #22 (2026-10-23): prose ~100-114 tok/s, reasoning ~140-155 tok/s, 40k-token prefill ~3,300 tok/s. tps is the conservative low end of the prose range. RAM not stated. Found via localmaxxing (discovery only); numbers re-checked at the original source on 2026-10-24.", "page_url": "https://getrigbase.com/builds/capy-flash-2x-rtx-pro-4000-threadripper-5975wx/" }, { "slug": "bnk-2x-tesla-v100-32gb-epyc-7402", "title": "Dual Tesla V100 32 GB on EPYC 7402 (bnk engine)", "gpus": "2x Tesla V100 32 GB (PG500-216, sm_70)", "vram_gb": 64, "cpu": "AMD EPYC 7402 (32 vCPUs)", "motherboard": "", "ram": "128 GB DDR4 (192 GB for DeepSeek)", "psu": "", "other": "PCIe 3.0 x16; Ubuntu 24.04, driver 580, CUDA 12.8", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.8 27B", "quant": "Q8_0", "engine": "bnk dense engine, tensor parallel, no drafting", "tps": 42.4, "how": "Maintainer decode tok/s from the bnk README", "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "Q8_0", "engine": "bnk dense engine, tensor parallel, MTP drafting", "tps": 104, "how": "Maintainer decode tok/s from the bnk README", "spec_decode": "MTP" }, { "model": "Qwen3.8 Flash-Next", "quant": "FP4-Local", "engine": "bnk v0.3.1, decode at 4K context", "tps": 114, "how": "Maintainer decode tok/s from the bnk README", "context": "4K", "spec_decode": "none" }, { "model": "Qwen3.8 Flash-Next", "quant": "IQ3_S", "engine": "bnk v0.3.1, decode at 4K context", "tps": 121, "how": "Maintainer decode tok/s from the bnk README", "context": "4K", "spec_decode": "none" }, { "model": "DeepSeek V4.1 Flash", "quant": "GSQ-RCO 2-bit", "engine": "bnk v0.5.0, experts split VRAM/RAM (CPU offload)", "tps": 13, "how": "Maintainer decode tok/s from the bnk README", "spec_decode": "none" } ], "source": "https://github.com/christ-pher/bnk", "note": "All figures from the bnk README (maintainer's own machine). The DeepSeek V4.1 Flash run needs 192 GB system RAM and reads the Engram tables from NVMe; '~13 tok/s' in the README. Found via localmaxxing (discovery only); numbers re-checked at the original source on 2026-10-24.", "page_url": "https://getrigbase.com/builds/bnk-2x-tesla-v100-32gb-epyc-7402/" }, { "slug": "intel-arc-pro-b70-neural-download-lab", "title": "Single Intel Arc Pro B70 32 GB (neural.download lab)", "gpus": "Intel Arc Pro B70 32 GB", "vram_gb": 32, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "One card of the lab's four-B70 Linux host; llama.cpp SYCL / oneAPI", "cost": null, "currency": "", "power": "", "results": [ { "model": "Gemma 4 26B-A4B", "quant": "UD-Q8_K_XL", "engine": "llama.cpp SYCL (pinned build, oneAPI 2026.0)", "tps": 122.16, "how": "Single-user, cache-cold decode median (lab package/repro README)", "spec_decode": "none" }, { "model": "Ornith 1.5 35B-A3B", "quant": "Q4_K_M", "engine": "llama.cpp SYCL + neural.download fusion patches; author marks this headline 'pending'", "tps": 131.46, "how": "Single-user, cache-cold decode median (lab package/repro README)", "status": "pending", "status_note": "neural.download marks this headline 'pending': speed measured, install checks not finished.", "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "Q4_K_M", "engine": "llama.cpp SYCL + neural.download TP1 stack, no speculation", "tps": 27.8, "how": "Single-user, cache-cold decode median (lab package/repro README)", "spec_decode": "none" } ], "source": "https://github.com/steveseguin/b70-optimization-lab", "source2": "https://neural.download/models/qwen38-27b-q4km-tp1-b70.html", "source3": "https://neural.download/models/ornith-15-35b-a3b-q4km-b70.html", "note": "Single-user, cache-cold decode medians from the lab's package/repro READMEs. Ornith 35B-A3B headline is marked 'pending' on neural.download (performance measured, install checks not finished). Host CPU/RAM not recorded in the package docs. Found via localmaxxing (discovery only); numbers re-checked at the original source on 2026-10-24.", "page_url": "https://getrigbase.com/builds/intel-arc-pro-b70-neural-download-lab/" }, { "slug": "2x-intel-arc-pro-b70-neural-download-lab", "title": "Dual Intel Arc Pro B70 (neural.download lab)", "gpus": "2x Intel Arc Pro B70 32 GB", "vram_gb": 64, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "Lab Linux host; SYCL", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.8 Flash-Next", "quant": "IQ3", "engine": "neural.download runtime, TP2", "tps": 58.56, "how": "Lab README/scoreboard", "spec_decode": "none" }, { "model": "Qwen3.8 27B", "quant": "FP8", "engine": "vLLM XPU, TP2, MTP 5", "tps": 46.01, "how": "Lab README/scoreboard", "spec_decode": "MTP" } ], "source": "https://github.com/steveseguin/b70-optimization-lab", "note": "From the lab README/scoreboard. TP1 on the same Flash-Next build: 31.5-31.8 tok/s. An older 90.3 tok/s 27B FP8 receipt did not reproduce and is excluded. Found via localmaxxing (discovery only); numbers re-checked at the original source on 2026-10-24.", "page_url": "https://getrigbase.com/builds/2x-intel-arc-pro-b70-neural-download-lab/" }, { "slug": "intel-arc-pro-b70-vllm-gptq-riceharvest", "title": "Single Intel Arc Pro B70 with vLLM XPU cookbook", "gpus": "Intel Arc Pro B70 32 GB", "vram_gb": 32, "cpu": "", "motherboard": "", "ram": "~12 GB free host RAM needed (embeddings in RAM)", "psu": "", "other": "vLLM XPU with B70 patches (ghcr.io/riceharvest/b70-vllm)", "cost": null, "currency": "", "power": "Stock 275 W cap", "results": [ { "model": "Qwen3.8 27B", "quant": "GPTQ Int4 (asym)", "engine": "vLLM XPU b70 image + MTP, reasoning prompt", "tps": 96, "how": "Greedy, one request at a time, 512-token answers (README \"Numbers\" table)", "context": "short prompt", "spec_decode": "MTP" }, { "model": "Qwen3.8 27B", "quant": "GPTQ Int4 (asym)", "engine": "vLLM XPU b70 image + MTP, prose prompt", "tps": 66, "how": "Greedy, one request at a time, 512-token answers (README \"Numbers\" table)", "context": "short prompt", "spec_decode": "MTP" }, { "model": "Qwen3.6 35B-A3B", "quant": "GPTQ Int4", "engine": "vLLM XPU b70 image, no MTP", "tps": 120, "how": "Greedy, one request at a time, 512-token answers (README \"Numbers\" table)", "context": "short prompt", "spec_decode": "none" } ], "source": "https://github.com/riceharvest/b70-qwen38-cookbook", "note": "Greedy, one request at a time, 512-token answers, short prompt (README 'Numbers' table). Code prompts reach 113 tok/s; at 32K context ~72 tok/s. Qwen3.6 35B-A3B figure is the README's '~120 tok/s without MTP'. Found via localmaxxing (discovery only); numbers re-checked at the original source on 2026-10-24.", "page_url": "https://getrigbase.com/builds/intel-arc-pro-b70-vllm-gptq-riceharvest/" }, { "slug": "8x-rtx-3090-deepseek-v4-flash-limechain", "title": "8x RTX 3090 serving DeepSeek V4 Flash (LimeChain vLLM kit)", "gpus": "8x RTX 3090 24 GB", "vram_gb": 192, "cpu": "", "motherboard": "", "ram": "", "psu": "", "other": "vLLM SM86 overlay, PP8/TP1, DSpark speculative decoding", "cost": null, "currency": "", "power": "", "results": [ { "model": "DeepSeek V4 Flash", "quant": "see source (FP8 KV cache)", "engine": "vLLM LimeChain SM86 overlay, 1 concurrent request", "tps": 95.52, "how": "Per-session end-to-end p50 at concurrency 1, 128 generated tokens", "spec_decode": "none" } ], "source": "https://github.com/LimeChain/vllm/tree/deepseek-v4-flash-sm86/release/limechain-sm86", "note": "Model is DeepSeek-V4-Flash-0731. Per-session end-to-end p50 at concurrency 1, 128 generated tokens; aggregate reaches ~404 tok/s at 12 concurrent. The README calls it screening-only. Found via localmaxxing (discovery only); numbers re-checked at the original source on 2026-10-24.", "page_url": "https://getrigbase.com/builds/8x-rtx-3090-deepseek-v4-flash-limechain/" }, { "slug": "4x-dgx-spark-glm-52-quanttrio", "title": "4x DGX Spark cluster running GLM-5.2 (unpruned)", "gpus": "4x Nvidia GB10 (unified, DGX Spark)", "vram_gb": 512, "cpu": "Nvidia Grace (per node)", "motherboard": "", "ram": "128 GB unified per node", "psu": "", "other": "ConnectX-7 RoCE between nodes; vLLM multi-node TP4, MTP k=5", "cost": null, "currency": "", "power": "", "results": [ { "model": "GLM-5.2", "quant": "QuantTrio Int4-Int8Mix", "engine": "vLLM TP4 + MTP, single-stream mean", "tps": 32.5, "how": "Single-stream mean (source config of 2026-07-17)", "spec_decode": "MTP", "context": "200K max" } ], "source": "https://github.com/tonyd2wild/GLM-5.2-QuantTrio-200K-4x-DGX-Spark", "note": "2026-07-17 config: 32.5 tok/s single-stream mean, 36.0 peak, 65-75 tok/s aggregate at 6 concurrent, 200K context. vram_gb is aggregate unified memory. Found via localmaxxing (discovery only); numbers re-checked at the original source on 2026-10-24.", "page_url": "https://getrigbase.com/builds/4x-dgx-spark-glm-52-quanttrio/" }, { "slug": "4x-dgx-spark-glm-53-flash-ringside", "title": "4x DGX Spark ring running GLM-5.3 Flash NVFP4", "gpus": "4x Nvidia GB10 (unified, DGX Spark)", "vram_gb": 512, "cpu": "Nvidia Grace 20-core (per node)", "motherboard": "", "ram": "128 GB unified per node", "psu": "", "other": "Switchless ConnectX-7 ring; vLLM TP4", "cost": null, "currency": "", "power": "", "results": [ { "model": "GLM-5.3 Flash", "quant": "NVFP4", "engine": "vLLM TP4 (RiNGSiDE), single-stream prose", "tps": 61.7, "how": "Single-stream decode reported by the source", "spec_decode": "none" }, { "model": "GLM-5.3 Flash", "quant": "NVFP4", "engine": "vLLM TP4 (RiNGSiDE), single-stream code", "tps": 105.3, "how": "Single-stream decode reported by the source", "spec_decode": "none" } ], "source": "https://github.com/othexmr/GLM-5.3-Flash-NVFP4-2x-4x-DGX-Sparks-RiNGSiDE", "note": "RedHatAI checkpoint; structured output reaches 145.4 tok/s. vram_gb is aggregate unified memory. Found via localmaxxing (discovery only); numbers re-checked at the original source on 2026-10-24.", "page_url": "https://getrigbase.com/builds/4x-dgx-spark-glm-53-flash-ringside/" }, { "slug": "2x-dgx-spark-glm-53-flash-ringside", "title": "2x DGX Spark running GLM-5.3 Flash NVFP4", "gpus": "2x Nvidia GB10 (unified, DGX Spark)", "vram_gb": 256, "cpu": "Nvidia Grace 20-core (per node)", "motherboard": "", "ram": "128 GB unified per node", "psu": "", "other": "ConnectX-7 link; vLLM TP2", "cost": null, "currency": "", "power": "", "results": [ { "model": "GLM-5.3 Flash", "quant": "NVFP4", "engine": "vLLM TP2 (RiNGSiDE), single-stream prose", "tps": 33.0, "how": "Single-stream decode reported by the source", "spec_decode": "none" }, { "model": "GLM-5.3 Flash", "quant": "NVFP4", "engine": "vLLM TP2 (RiNGSiDE), single-stream code", "tps": 56.5, "how": "Single-stream decode reported by the source", "spec_decode": "none" } ], "source": "https://github.com/othexmr/GLM-5.3-Flash-NVFP4-2x-4x-DGX-Sparks-RiNGSiDE", "note": "NVIDIA checkpoint; structured output 83.4 tok/s. vram_gb is aggregate unified memory. Found via localmaxxing (discovery only); numbers re-checked at the original source on 2026-10-24.", "page_url": "https://getrigbase.com/builds/2x-dgx-spark-glm-53-flash-ringside/" }, { "slug": "dgx-spark-qwen36-27b-nvfp4-dflash", "title": "DGX Spark with Qwen3.6 27B NVFP4 + DFlash speculative decoding", "gpus": "Nvidia GB10 (unified)", "vram_gb": 128, "cpu": "Nvidia Grace (GB10)", "motherboard": "", "ram": "128 GB unified", "psu": "", "other": "vLLM + z-lab DFlash drafter, thinking on", "cost": null, "currency": "", "power": "", "results": [ { "model": "Qwen3.6 27B", "quant": "NVFP4", "engine": "vLLM + DFlash spec decoding", "tps": 32.83, "how": "Median decode, Docker reproduction", "spec_decode": "DFlash" }, { "model": "Qwen3.6 27B", "quant": "FP8", "engine": "vLLM autoregressive baseline", "tps": 7.85, "how": "Autoregressive baseline, same reproduction", "spec_decode": "none" } ], "source": "https://github.com/my-other-github-account/qwen36-dflash-spark-repro", "note": "Docker reproduction; median decode 32.83 tok/s (mean 40.10). Thinking off drops speed sharply. Found via localmaxxing (discovery only); numbers re-checked at the original source on 2026-10-24.", "page_url": "https://getrigbase.com/builds/dgx-spark-qwen36-27b-nvfp4-dflash/" } ] }