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Can the NVIDIA RTX PRO 6000 Blackwell run Devstral Small 2505?

77 ptsGreat (A) · 77 of 100 points: Model quality 29.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.6/15

Yes, it runs well

Runs well. At Q8_0 it needs about 31.7 GB of 96.0 GB usable, with an estimated speed of 47.5 tok/s.

Best quant
Q8_0
Generation speed
47.5 tok/s
Prompt processing
3,818 tok/s
First token (1K prompt)
0.3 s
Largest safe context
128K

Download Devstral Small 2505

Recommended for the NVIDIA RTX PRO 6000 Blackwell: Q8_0. 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 devstral:24b-small-2505-q8_0

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 Q8_0 file (opens in a new tab)

huggingface.co/unsloth/Devstral-Small-2505-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Devstral-Small-2505-GGUF:Q8_0

Original weights · Hugging Face

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

huggingface.co/mistralai/Devstral-Small-2505 (opens in a new tab)

No sign-in needed.

How the A 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.29.7 / 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.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.7.6 / 15
Total77.3 / 10077 ptsGreat (A) · 77 of 100 points: Model quality 29.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.6/15

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 16.2 GB 96.0 GB 153 tok/s Runs well 64 ptsGood (B) · 64 of 100 points: Model quality 20.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.9/15 GGUF (opens in a new tab)
Q3_K_M 18.3 GB 96.0 GB 121 tok/s Runs well 69 ptsGood (B) · 69 of 100 points: Model quality 24.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.4/15 GGUF (opens in a new tab)
Q4_K_M 21.1 GB 96.0 GB 93.3 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 28.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.1/15 GGUF (opens in a new tab)
Q5_K_M 23.5 GB 96.0 GB 77.1 tok/s Runs well 74 ptsGreat (A) · 74 of 100 points: Model quality 28.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.6/15 GGUF (opens in a new tab)
Q6_K 26.1 GB 96.0 GB 64.8 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 29.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.3/15 GGUF (opens in a new tab)
Q8_0 31.7 GB 96.0 GB 47.5 tok/s Runs well 77 ptsGreat (A) · 77 of 100 points: Model quality 29.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.6/15 GGUF (opens in a new tab)
F16 53.9 GB 96.0 GB 21.9 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 29.7/45, Memory fit 25.0/25, Speed 11.1/15, Memory use 13.0/15 GGUF (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.