Can I run it?

Can I run Qwen3.8 35B A3B Distill on NVIDIA H100 SXM 80GB?

Tight fit

Tight fit fully on your NVIDIA H100 SXM 80GB at BF16, 8K context, about 298–407 tok/s. Weights 66.2 GB plus 0.19 GB of KV cache — 67.3 GB of 76.0 GB usable. Up to 256K context still fits.

Qwen3.8 35B A3B Distill details · NVIDIA H100 SXM 80GB page · How this estimate is made

Run commands

llama-server -hf empero-ai/Qwen3.8-35B-A3B-Distill-GGUF:BF16 -c 8192 -ngl 40

ollama run qwen3.8

lms get empero-ai/Qwen3.8-35B-A3B-Distill-GGUF
# Then load quant BF16

Assumptions

• Estimate, not a benchmark. Decode speed uses a memory-bandwidth model at 55–75% of peak.
• KV cache is FP16. Engine overhead assumes llama.cpp (0.3 GB + 1% of the weights). Ollama is closer to 0.9 GB + 3%, LM Studio closer to 1 GB + 3%.
• BF16 size is the real GGUF file size from Hugging Face.
• CPU runs keep about 4 GB plus a 10% cushion for the OS.
• Speed is quoted at 8K context, not at the maximum context that still fits.

Larger models that also fit

• Can I run Qwen3.8 Flash Next on NVIDIA H100 SXM 80GB? — Tight fit, UD-IQ1_M, 144–196 tok/s
• Can I run Cyber Tiel Coder 35B A3B GGUF MTP on NVIDIA H100 SXM 80GB? — Runs great, Q8_0, 549–749 tok/s
• Can I run gpt oss 120b on NVIDIA H100 SXM 80GB? — Runs, UD-Q8_K_XL, 150–204 tok/s

Updated 2026-10-24. Profile on this page is the device default plus 32 GB of DDR5-5600 system RAM.