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Can the NVIDIA GB10 (128 GB) run Qwen3.6 35B-A3B?

89 ptsExcellent (S) · 89 of 100 points: Model quality 41.7/45, Memory fit 25.0/25, Speed 14.8/15, Memory use 7.9/15

Yes, it runs well

Runs well. At Q6_K it needs about 39.5 GB of 115 GB usable, with an estimated speed of 38.8 tok/s.

Best quant
Q6_K
Generation speed
38.8 tok/s
Prompt processing
7,440 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Download Qwen3.6 35B-A3B

Recommended for the NVIDIA GB10 (128 GB): Q6_K. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run qwen3.6:35b

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q6_K file (opens in a new tab)

huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen3.6-35B-A3B-GGUF:Q6_K

Original weights · Hugging Face

Full-precision files from Alibaba, for fine-tuning, vLLM, Transformers.

huggingface.co/Qwen/Qwen3.6-35B-A3B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Alibaba. The weights themselves download from Hugging Face.

github.com/QwenLM/Qwen3.8 (opens in a new tab)

git clone https://github.com/QwenLM/Qwen3.8

Qwen’s current repo: Qwen3.5 and Qwen3.6 redirect here.

Links checked 5 days ago against Hugging Face, Ollama and GitHub.

How the S grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.41.7 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.14.8 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.7.9 / 15
Total89.3 / 10089 ptsExcellent (S) · 89 of 100 points: Model quality 41.7/45, Memory fit 25.0/25, Speed 14.8/15, Memory use 7.9/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the NVIDIA GB10 (128 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 24.4 GB 115 GB 91.7 tok/s Runs well 74 ptsGreat (A) · 74 of 100 points: Model quality 29.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.9/15 GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M 27.6 GB 115 GB 72.2 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 35.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.5/15 GGUF (opens in a new tab)
Q4_K_M 31.9 GB 115 GB 55.9 tok/s Runs well 86 ptsExcellent (S) · 86 of 100 points: Model quality 40.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.4/15 GGUF (opens in a new tab)
Q5_K_M 35.5 GB 115 GB 46.1 tok/s Runs well 87 ptsExcellent (S) · 87 of 100 points: Model quality 40.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.1/15 GGUF (opens in a new tab)
Q6_K 39.5 GB 115 GB 38.8 tok/s Runs well 89 ptsExcellent (S) · 89 of 100 points: Model quality 41.7/45, Memory fit 25.0/25, Speed 14.8/15, Memory use 7.9/15 GGUF (opens in a new tab)
Q8_0 48.1 GB 115 GB 28.4 tok/s Runs well 89 ptsExcellent (S) · 89 of 100 points: Model quality 42.1/45, Memory fit 25.0/25, Speed 12.6/15, Memory use 9.6/15 GGUF (opens in a new tab)
F16 81.9 GB 115 GB 13.1 tok/s Runs well 90 ptsExcellent (S) · 90 of 100 points: Model quality 42.1/45, Memory fit 25.0/25, Speed 8.6/15, Memory use 15.0/15 GGUF · folder (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.