Best home rig for Qwen3.8 Flash-Next
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Real, measured generation speeds for Qwen3.8 Flash-Next on home hardware, fastest first. Different quants/engines aren't directly comparable.
Memory math: how much VRAM / RAM does Qwen3.8 Flash-Next need?
Qwen3.8 Flash-Next has 180B parameters, about 6B active per token (source: ~180B total per the model card as summarised by strata→signal (125B main network, ~6B active per token, 51B lookup table, 4B draft head)). Weight size = parameters × bits-per-weight ÷ 8, before context (KV cache) and runtime overhead.
| Format | Bits/weight | Weights | Smallest common memory size that holds the weights alone |
|---|---|---|---|
| Q8_0 | 8.50 | 191.3 GB | 192 GB |
| Q6_K | 6.56 | 147.7 GB | 192 GB |
| Q5_K_M | 5.70 | 128.3 GB | 192 GB |
| Q4_K_M | 4.89 | 110.1 GB | 128 GB |
| Q3_K_M | 4.00 | 89.9 GB | 96 GB |
| Q2_K | 3.16 | 71.1 GB | 96 GB |
Bits-per-weight for GGUF quants are llama.cpp's own figures (llama.cpp quantize README). "As measured" rows apply the source's stated bits-per-weight to the parameter count; real files differ slightly because some layers stay at higher precision. Try other combinations in the VRAM calculator.
Why it can still run on smaller GPUs: it is a mixture-of-experts model, so only about 6B of its 180B parameters are used for each generated token. Runtimes such as llama.cpp (--n-cpu-moe), ExLlamaV3 or Project Maya can keep most expert weights in system RAM and only the shared layers and hot experts in VRAM. Then you need VRAM + system RAM ≥ weights, and speed is limited mainly by RAM bandwidth.
Measured on setups with less GPU memory than the Q4_K_M weights (so part of the model lived in system RAM or was streamed from disk):
- RTX 5070 12GB (12 GB) + 32 GB DDR4-2400: 11.5 tok/s (UD-IQ3_XXS, experts streamed from RAM/SSD)
- RTX 5070 12GB (12 GB) + 32 GB DDR4-2400: 10.15 tok/s (UD-IQ3_XXS, long coding prompt (4,892 tokens out))
- RTX 5090 (32 GB) + 128 GB DDR5-5600 (4x32 GB): 78.8 tok/s (Unsloth UD-Q5_K_XL, MTP drafting, experts offloaded to RAM)
- RTX 5090 (32 GB): 159.2 tok/s (IQ3_S (~55 GB), experts offloaded to RAM, speculation helper)
- 2x RTX 5070 Ti 16 GB (32 GB) + 96 GB DDR5-5600: 182 tok/s (GSQ-RCO (64 GB edition))
- 2x RTX 5070 Ti 16 GB (32 GB) + 96 GB DDR5-5600: 155 tok/s (NVFP4)
FAQ
How much VRAM does Qwen3.8 Flash-Next need?
The weights are about 110.1 GB at Q4_K_M (180B params × 4.89 bits ÷ 8, before context/KV cache). Add memory for context (KV cache) and the runtime. The smallest-memory setup with a measured result here, fully in memory, is DGX Spark (GB10) (128 GB) at 26.8 tok/s.
What is the fastest measured hardware for Qwen3.8 Flash-Next?
EPYC 7532 workstation (RTX Pro 6000 + RTX 3090 Ti, 300 W caps): Qwen3.8-Flash-Next, llama.cpp vs Strata engine: 205.263 tok/s generation (Strata engine 0.1.39, RTX Pro 6000, IQ3_S + model draft head, all experts on the RTX Pro 6000), from https://research.strata2signal.com/flash-next-on-three-cards/.
Which GPUs have measured results for Qwen3.8 Flash-Next?
2x Intel Arc Pro B70, 2x RTX 5070 Ti, 2x RTX 5080, 2x RTX PRO 4000 Blackwell, 2x Tesla V100 32GB, DGX Spark (GB10), RTX 5090.
Can it run on… 2x Intel Arc Pro B70 · 2x RTX 5070 Ti · 2x RTX 5080 · 2x RTX PRO 4000 Blackwell · 2x Tesla V100 32GB · DGX Spark (GB10) · RTX 5090
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