on

Can the NVIDIA RTX PRO 6000 Blackwell run QwQ 32B?

83 ptsGreat (A) · 83 of 100 points: Model quality 34.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.3/15

Yes, it runs well

Runs well. At Q6_K it needs about 34.4 GB of 96.0 GB usable, with an estimated speed of 46.6 tok/s.

Best quant
Q6_K
Generation speed
46.6 tok/s
Prompt processing
2,747 tok/s
First token (1K prompt)
0.4 s
Largest safe context
40K

Download QwQ 32B

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 qwq:32b

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/Qwen/QwQ-32B-GGUF (opens in a new tab)

ollama run hf.co/Qwen/QwQ-32B-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/Qwen/QwQ-32B (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/QwQ (opens in a new tab)

git clone https://github.com/QwenLM/QwQ

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.34.7 / 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.8.3 / 15
Total83.0 / 10083 ptsGreat (A) · 83 of 100 points: Model quality 34.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.3/15

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 20.7 GB 96.0 GB 110 tok/s Runs well 69 ptsGood (B) · 69 of 100 points: Model quality 24.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.0/15 GGUF files (opens in a new tab)
Q3_K_M 23.6 GB 96.0 GB 86.8 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 29.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.7/15 GGUF files (opens in a new tab)
Q4_K_M 27.5 GB 96.0 GB 67.1 tok/s Runs well 79 ptsGreat (A) · 79 of 100 points: Model quality 33.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.6/15 GGUF (opens in a new tab)
Q5_K_M 30.8 GB 96.0 GB 55.5 tok/s Runs well 81 ptsGreat (A) · 81 of 100 points: Model quality 34.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.4/15 GGUF (opens in a new tab)
Q6_K 34.4 GB 96.0 GB 46.6 tok/s Runs well 83 ptsGreat (A) · 83 of 100 points: Model quality 34.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.3/15 GGUF (opens in a new tab)
Q8_0 42.3 GB 96.0 GB 34.2 tok/s Runs well 84 ptsGreat (A) · 84 of 100 points: Model quality 35.1/45, Memory fit 25.0/25, Speed 13.9/15, Memory use 10.2/15 GGUF (opens in a new tab)
F16 73.1 GB 96.0 GB 15.8 tok/s Runs well 84 ptsGreat (A) · 84 of 100 points: Model quality 35.1/45, Memory fit 25.0/25, Speed 9.4/15, 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.