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Best local LLMs for 32 GB of VRAM or unified memory

Measured speeds from builds where the model ran on more than 24 and up to 32 GB of GPU or unified memory. Each number links to its build and source.

8 GB · 12 GB · 16 GB · 24 GB · 32 GB · 48 GB · 64 GB · 96 GB · 128 GB or more

GPUs in this tier: RTX 5090 · Radeon AI Pro R9700 · Intel Arc Pro B70 · Tesla V100 32GB

Fastest measured result per model

ModelPlain decode tok/sWith speculative decoding ⚡Results
Qwen3.6 35B-A3B258 (llama-bench tg128 (quant per result), RTX 5090)–8
Qwen3.8 27B75.2 (Q4_K_M (EPYC 9534 host), RTX 5090)230 (3-bit GSQ, 2x RTX 5070 Ti)8
Qwen3.6 27B75.7 (llama-bench tg128 (quant per result), RTX 5090)66.0 (Q4_K_M + MTP draft head (draft-n-max 3), 64K context, Radeon AI Pro R9700)6
Qwen3.8 Flash-Next––6
gpt-oss 20B324.2 (reported as Q4_K_M, RTX 5090)–4
Llama 3.2 3B271.4 (, RTX 5090)–3
Gemma 4 26B-A4B235 (llama-bench tg128 (quant per result), RTX 5090)–2
DeepSeek R1 Distill 14B53.72 (, Radeon AI Pro R9700)–1
Gemma 4 31B70.0 (llama-bench tg128 (quant per result), RTX 5090)–1
Gemma 4 12B142 (llama-bench tg128 (quant per result), RTX 5090)–1
Qwen3.5 9B208 (llama-bench tg128 (quant per result), RTX 5090)–1
Ornith 1.5 122B––1
GLM-5.3 Flash––1
DeepSeek V4.1 Flash––1
Ornith 1.5 35B-A3B––1

Excludes results the source marks pending and results where part of the model sat in system RAM.

All results in this tier

(MTP, DFlash, DSpark and similar draft-and-verify methods give much higher tok/s on predictable text and are not comparable 1:1 with plain decode)

ModelBuildRan onQuantEnginetok/sDetails
DeepSeek R1 Distill 14BIntel Core Ultra 265K + Radeon AI Pro R9700Radeon AI Pro R9700 (32 GB)llama.cpp53.72
DeepSeek V4.1 FlashRyzen 9 9950X3D + RTX 5090 + 128 GB DDR5: four big MoEs with experts offloaded to RAM (Qwen3.8 Flash-Next 78.8 tok/s)RTX 5090 (32 GB)Maya-S (experts 2-3 bit), partly streamed from SSDProject Maya 1.1.21 (maya-ds4)24.9partly in system RAM
GLM-5.3 FlashRyzen 9 9950X3D + RTX 5090 + 128 GB DDR5: four big MoEs with experts offloaded to RAM (Qwen3.8 Flash-Next 78.8 tok/s)RTX 5090 (32 GB)EXL3 3.05 bpw, MTP drafting, experts offloaded to RAMProject Maya 1.1.2130.1⚡ spec-decode (MTP)partly in system RAM
Gemma 4 12BRTX 5090: 2026 models (llamabench community)RTX 5090 (32 GB)llama-bench tg128 (quant per result)llama.cpp142
Gemma 4 26B-A4BRTX 5090: 2026 models (llamabench community)RTX 5090 (32 GB)llama-bench tg128 (quant per result)llama.cpp235
Gemma 4 26B-A4BSingle Intel Arc Pro B70 32 GB (neural.download lab)Intel Arc Pro B70 (32 GB)UD-Q8_K_XLllama.cpp SYCL (pinned build, oneAPI 2026.0)122.16📏 Single-user, cache-cold decode median (lab package/repro README)
Gemma 4 31BRTX 5090: 2026 models (llamabench community)RTX 5090 (32 GB)llama-bench tg128 (quant per result)llama.cpp70.0
Llama 3.2 3BIntel i9-13900K + RTX 5090RTX 5090 (32 GB)llama.cpp271.4
Llama 3.2 3BIntel Core Ultra 265K + Radeon AI Pro R9700Radeon AI Pro R9700 (32 GB)llama.cpp195.91
Llama 3.2 3BM4 Mac mini (10-core) 32GBApple M4 (32 GB)llama.cpp41.31
Ornith 1.5 122BRyzen 9 9950X3D + RTX 5090 + 128 GB DDR5: four big MoEs with experts offloaded to RAM (Qwen3.8 Flash-Next 78.8 tok/s)RTX 5090 (32 GB)EXL3 4 bpw, 180/256 experts per layer in RAM, no draftingTabbyAPI + ExLlamaV3 1.6.246.4partly in system RAM
Ornith 1.5 35B-A3BSingle Intel Arc Pro B70 32 GB (neural.download lab)Intel Arc Pro B70 (32 GB)Q4_K_Mllama.cpp SYCL + neural.download fusion patches; author marks this headline 'pending'131.46⏳ author marks pending📏 Single-user, cache-cold decode median (lab package/repro README)
Qwen3.5 9BRTX 5090: 2026 models (llamabench community)RTX 5090 (32 GB)llama-bench tg128 (quant per result)llama.cpp208
Qwen3.6 27BRTX 5090: 2026 models (llamabench community)RTX 5090 (32 GB)llama-bench tg128 (quant per result)llama.cpp75.7
Qwen3.6 27BRTX 5090 (llm-speed signed runs, Oct 2026): gpt-oss 20B 324 tok/s, Qwen3.8 27B 75 tok/sRTX 5090 (32 GB)Q4_K_Mllama.cpp 0.7.0-dev75.1
Qwen3.6 27BRadeon AI Pro R9700: Qwen3.6 27B on Vulkan, HIP and Ollama, with and without MTPRadeon AI Pro R9700 (32 GB)Q4_K_M + MTP draft head (draft-n-max 3), 64K contextllama.cpp Vulkan66.0⚡ spec-decode (MTP)
Qwen3.6 27BRadeon AI Pro R9700: Qwen3.6 27B on Vulkan, HIP and Ollama, with and without MTPRadeon AI Pro R9700 (32 GB)Q4_K_M, no MTPllama.cpp Vulkan31.9
Qwen3.6 27BRadeon AI Pro R9700: Qwen3.6 27B on Vulkan, HIP and Ollama, with and without MTPRadeon AI Pro R9700 (32 GB)Q4_K_Mllama.cpp HIP (ROCm)27.1
Qwen3.6 27BRadeon AI Pro R9700: Qwen3.6 27B on Vulkan, HIP and Ollama, with and without MTPRadeon AI Pro R9700 (32 GB)Q4_K_MOllama (HIP)26.7
Qwen3.6 35B-A3BRTX 5090: 2026 models (llamabench community)RTX 5090 (32 GB)llama-bench tg128 (quant per result)llama.cpp258
Qwen3.6 35B-A3BSingle Intel Arc Pro B70 with vLLM XPU cookbookIntel Arc Pro B70 (32 GB)GPTQ Int4vLLM XPU b70 image, no MTP120context: short prompt📏 Greedy, one request at a time, 512-token answers (README "Numbers" table)
Qwen3.6 35B-A3BRadeon AI Pro R9700 (one card): Qwen3.6 35B-A3B from 4K to 256K context (AMDai lab rig)Radeon AI Pro R9700 (32 GB)Q4_K_Mllama.cpp Vulkan117.69
Qwen3.6 35B-A3BRadeon AI Pro R9700 (one card): Qwen3.6 35B-A3B from 4K to 256K context (AMDai lab rig)Radeon AI Pro R9700 (32 GB)Q5_K_M (24.68 GB VRAM)llama.cpp Vulkan109.73
Qwen3.6 35B-A3BRadeon AI Pro R9700 (one card): Qwen3.6 35B-A3B from 4K to 256K context (AMDai lab rig)Radeon AI Pro R9700 (32 GB)Unsloth UD-Q5_K_XL, short context, f16 KVllama.cpp Vulkan102.39
Qwen3.6 35B-A3BRadeon AI Pro R9700 (one card): Qwen3.6 35B-A3B from 4K to 256K context (AMDai lab rig)Radeon AI Pro R9700 (32 GB)UD-Q5_K_XL, 32K contextllama.cpp Vulkan90.52
Qwen3.6 35B-A3BRadeon AI Pro R9700 (one card): Qwen3.6 35B-A3B from 4K to 256K context (AMDai lab rig)Radeon AI Pro R9700 (32 GB)UD-Q5_K_XL, 128K contextllama.cpp Vulkan66.43
Qwen3.6 35B-A3BRadeon AI Pro R9700 (one card): Qwen3.6 35B-A3B from 4K to 256K context (AMDai lab rig)Radeon AI Pro R9700 (32 GB)UD-Q5_K_XL, 256K contextllama.cpp Vulkan44.4
Qwen3.8 27BDual RTX 5070 Ti on Ryzen 9 9900X (capy-flash maintainer rig)2x RTX 5070 Ti (32 GB)3-bit GSQcapy-flash, 2 GPUs, MTP, decode at 8k prompt230⚡ spec-decode (MTP)context: 8k prompt📏 Maintainer/user decode tok/s from the capy-flash README
Qwen3.8 27BSingle Intel Arc Pro B70 with vLLM XPU cookbookIntel Arc Pro B70 (32 GB)GPTQ Int4 (asym)vLLM XPU b70 image + MTP, reasoning prompt96⚡ spec-decode (MTP)context: short prompt📏 Greedy, one request at a time, 512-token answers (README "Numbers" table)
Qwen3.8 27BRTX 5090 (llm-speed signed runs, Oct 2026): gpt-oss 20B 324 tok/s, Qwen3.8 27B 75 tok/sRTX 5090 (32 GB)Q4_K_M (EPYC 9534 host)llama.cpp 0.7.0-dev (b11669)75.2
Qwen3.8 27BSingle Intel Arc Pro B70 with vLLM XPU cookbookIntel Arc Pro B70 (32 GB)GPTQ Int4 (asym)vLLM XPU b70 image + MTP, prose prompt66⚡ spec-decode (MTP)context: short prompt📏 Greedy, one request at a time, 512-token answers (README "Numbers" table)
Qwen3.8 27BRadeon AI Pro R9700: Qwen3.8 27B on llama.cpp, Ollama and vLLM (Vadi Taslim)Radeon AI Pro R9700 (32 GB)Q4_K_M + MTP speculative decoding, reasoning onllama.cpp Vulkan57⚡ spec-decode (MTP)
Qwen3.8 27BRadeon AI Pro R9700: Qwen3.8 27B on llama.cpp, Ollama and vLLM (Vadi Taslim)Radeon AI Pro R9700 (32 GB)Q4_K_MOllama30
Qwen3.8 27BSingle Intel Arc Pro B70 32 GB (neural.download lab)Intel Arc Pro B70 (32 GB)Q4_K_Mllama.cpp SYCL + neural.download TP1 stack, no speculation27.8📏 Single-user, cache-cold decode median (lab package/repro README)
Qwen3.8 27BRadeon AI Pro R9700: Qwen3.8 27B on llama.cpp, Ollama and vLLM (Vadi Taslim)Radeon AI Pro R9700 (32 GB)AWQ INT4, single requestvLLM (radiance)22
Qwen3.8 Flash-NextDual RTX 5070 Ti on Ryzen 9 9900X (capy-flash maintainer rig)2x RTX 5070 Ti (32 GB)GSQ-RCO (64 GB edition)capy-flash v0.7.0, 2 GPUs, agentic decode182📏 Maintainer/user decode tok/s from the capy-flash READMEpartly in system RAM
Qwen3.8 Flash-NextRTX 5090 workstation: Qwen3.8 Flash-Next IQ3_S at 159.2 tok/s on Strata (expert offload)RTX 5090 (32 GB)IQ3_S (~55 GB), experts offloaded to RAM, speculation helperStrata159.2partly in system RAM
Qwen3.8 Flash-NextDual RTX 5070 Ti on Ryzen 9 9900X (capy-flash maintainer rig)2x RTX 5070 Ti (32 GB)NVFP4capy-flash v0.6.0, 2 GPUs, agentic decode155📏 Maintainer/user decode tok/s from the capy-flash READMEpartly in system RAM
Qwen3.8 Flash-NextDual RTX 5080 on EPYC 7F52 (capy-flash user report)2x RTX 5080 (32 GB)NVFP4capy-flash v0.7.2, --expert-cpu-threads 15, agentic decode144.4📏 User-reported (issue #11), listed in the capy-flash READMEpartly in system RAM
Qwen3.8 Flash-NextDual RTX 5080 on EPYC 7F52 (capy-flash user report)2x RTX 5080 (32 GB)NVFP4capy-flash v0.7.2 (CUDA 13.4), agentic decode141.3📏 User-reported (issue #11), listed in the capy-flash READMEpartly in system RAM
Qwen3.8 Flash-NextRyzen 9 9950X3D + RTX 5090 + 128 GB DDR5: four big MoEs with experts offloaded to RAM (Qwen3.8 Flash-Next 78.8 tok/s)RTX 5090 (32 GB)Unsloth UD-Q5_K_XL, MTP drafting, experts offloaded to RAMStrata (296b59d)78.8⚡ spec-decode (MTP)partly in system RAM
gpt-oss 20BRTX 5090 (llm-speed signed runs, Oct 2026): gpt-oss 20B 324 tok/s, Qwen3.8 27B 75 tok/sRTX 5090 (32 GB)reported as Q4_K_Mllama.cpp 0.7.0-dev324.2
gpt-oss 20BRTX 5090: 2026 models (llamabench community)RTX 5090 (32 GB)llama-bench tg128 (quant per result)llama.cpp313
gpt-oss 20BSingle RTX 5090 32GBRTX 5090 (32 GB)MXFP4llama.cpp282.51
gpt-oss 20BIntel Core Ultra 265K + Radeon AI Pro R9700Radeon AI Pro R9700 (32 GB)llama.cpp163.26

What fits by memory math

Q4_K_M weights (4.8944 bits per weight, per the llama.cpp quantize table) that fit in 32 GB with ~5% headroom, before KV cache: Flint 40B-A2.5B ≈24 GB, Ornith 1.5 35B-A3B ≈22 GB, Qwen3.6 35B-A3B ≈22 GB, Gemma 4 31B ≈19 GB, Nemotron 3.5 Lightning 30B-A3B ≈18 GB, Qwen3.6 27B ≈17 GB, Qwen3.8 27B ≈17 GB, Gemma 4 26B-A4B ≈15 GB, Devstral Small 2 24B ≈15 GB, LFM2.5 24B-A2B ≈15 GB, gpt-oss 20B ≈13 GB, Gemma 4 12B ≈7 GB, Qwen3.5 9B ≈5 GB, Llama 3.1 8B ≈5 GB.

FAQ

Which local LLMs have been measured on 32 GB of VRAM?

gpt-oss 20B, DeepSeek R1 Distill 14B, Llama 3.2 3B, Qwen3.6 35B-A3B, Qwen3.6 27B, Gemma 4 31B, Gemma 4 26B-A4B, Gemma 4 12B, Qwen3.5 9B, Qwen3.8 27B, Qwen3.8 Flash-Next, Ornith 1.5 122B, GLM-5.3 Flash, DeepSeek V4.1 Flash, Ornith 1.5 35B-A3B (45 results).

What fits in 32 GB of VRAM?

By memory math at Q4_K_M (4.8944 bits per weight, llama.cpp), weights only, before KV cache and context: Flint 40B-A2.5B (≈24 GB), Ornith 1.5 35B-A3B (≈22 GB), Qwen3.6 35B-A3B (≈22 GB), Gemma 4 31B (≈19 GB), Nemotron 3.5 Lightning 30B-A3B (≈18 GB), Qwen3.6 27B (≈17 GB), Qwen3.8 27B (≈17 GB), Gemma 4 26B-A4B (≈15 GB). Leave a few GB for context.

Are speculative-decoding results comparable?

Not 1:1. MTP, DFlash and similar methods draft tokens that the main model verifies, which can double tok/s on predictable text. This page lists them in a separate column; tick the box to hide them in the full table.

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