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Can the NVIDIA RTX PRO 6000 Blackwell run Mistral Small 4 (119B-A6B)?

89 ptsExcellent (S) · 89 of 100 points: Model quality 34.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 78.4 GB of 96.0 GB usable, with an estimated speed of 183 tok/s.

Best quant
Q4_K_M
Generation speed
183 tok/s
Prompt processing
15,000 tok/s
First token (1K prompt)
0.3 s
Largest safe context
753K

Download Mistral Small 4 (119B-A6B)

Recommended for the NVIDIA RTX PRO 6000 Blackwell: Q4_K_M. Every other quantization has its own download link in the table below.

Quantized files · GGUF

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

Download the Q4_K_M files (opens in a new tab)

huggingface.co/unsloth/Mistral-Small-4-119B-2603-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Mistral-Small-4-119B-2603-GGUF

Original weights · Hugging Face

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

huggingface.co/mistralai/Mistral-Small-4-119B-2603 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/mistralai/mistral-inference (opens in a new tab)

git clone https://github.com/mistralai/mistral-inference

Mistral AI’s official inference code.

Links checked 9 hours 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.34.5 / 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.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Total89.5 / 10089 ptsExcellent (S) · 89 of 100 points: Model quality 34.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

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

Every quantization on the NVIDIA RTX PRO 6000 Blackwell

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 53.3 GB 96.0 GB 301 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 25.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.8/15 GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M 64.1 GB 96.0 GB 237 tok/s Runs well 85 ptsExcellent (S) · 85 of 100 points: Model quality 30.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF · folder (opens in a new tab)
Q4_K_M 78.4 GB 96.0 GB 183 tok/s Runs well 89 ptsExcellent (S) · 89 of 100 points: Model quality 34.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF · folder (opens in a new tab)
Q5_K_M 90.4 GB 96.0 GB 151 tok/s Tight fit 89 ptsExcellent (S) · 89 of 100 points: Model quality 35.2/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF · folder (opens in a new tab)
Q6_K 103 GB 96.0 GB 42.7 tok/s Runs with offload 75 ptsGreat (A) · 75 of 100 points: Model quality 35.9/45, Memory fit 10.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF · folder (opens in a new tab)
Q8_0 132 GB 96.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF · folder (opens in a new tab)
F16 244 GB 96.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.