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Can the NVIDIA RTX PRO 6000 Blackwell run Olmo 3.1 32B Think?

81 ptsGreat (A) · 81 of 100 points: Model quality 33.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.8/15

Yes, it runs well

Runs well. At Q6_K it needs about 32.6 GB of 96.0 GB usable, with an estimated speed of 47.4 tok/s.

Best quant
Q6_K
Generation speed
47.4 tok/s
Prompt processing
2,792 tok/s
First token (1K prompt)
0.4 s
Largest safe context
64K

Download Olmo 3.1 32B Think

Recommended for the NVIDIA RTX PRO 6000 Blackwell: 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 olmo-3.1:32b-think

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/Olmo-3.1-32B-Think-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Olmo-3.1-32B-Think-GGUF:Q6_K

Original weights · Hugging Face

Full-precision files from Ai2, for fine-tuning, vLLM, Transformers.

huggingface.co/allenai/Olmo-3.1-32B-Think (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Ai2. The weights themselves download from Hugging Face.

github.com/allenai/OLMo (opens in a new tab)

git clone https://github.com/allenai/OLMo

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.33.9 / 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.7.8 / 15
Total81.7 / 10081 ptsGreat (A) · 81 of 100 points: Model quality 33.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.8/15

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 19.0 GB 96.0 GB 112 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 23.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.6/15 GGUF (opens in a new tab)
Q3_K_M 21.9 GB 96.0 GB 88.2 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 28.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.3/15 GGUF (opens in a new tab)
Q4_K_M 25.8 GB 96.0 GB 68.3 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 32.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.2/15 GGUF (opens in a new tab)
Q5_K_M 29.0 GB 96.0 GB 56.4 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 33.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.0/15 GGUF (opens in a new tab)
Q6_K 32.6 GB 96.0 GB 47.4 tok/s Runs well 81 ptsGreat (A) · 81 of 100 points: Model quality 33.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.8/15 GGUF (opens in a new tab)
Q8_0 40.3 GB 96.0 GB 34.7 tok/s Runs well 82 ptsGreat (A) · 82 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 14.0/15, Memory use 9.7/15 GGUF (opens in a new tab)
F16 70.6 GB 96.0 GB 16.0 tok/s Runs well 83 ptsGreat (A) · 83 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 9.5/15, 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.