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Can the NVIDIA RTX PRO 6000 Blackwell run Qwen3 Next 80B-A3B Instruct?

92 ptsExcellent (S) · 92 of 100 points: Model quality 37.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q6_K it needs about 72.3 GB of 96.0 GB usable, with an estimated speed of 254 tok/s.

Best quant
Q6_K
Generation speed
254 tok/s
Prompt processing
30,000 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Download Qwen3 Next 80B-A3B Instruct

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 qwen3-next:80b

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/Qwen3-Next-80B-A3B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/Qwen/Qwen3-Next-80B-A3B-Instruct-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct (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 (opens in a new tab)

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

Qwen’s main repo, renamed from Qwen2.5 to Qwen3; it still documents Qwen2.5.

Links checked 7 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.37.4 / 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
Total92.4 / 10092 ptsExcellent (S) · 92 of 100 points: Model quality 37.4/45, 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 38.1 GB 96.0 GB 602 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 26.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.2/15 GGUF files (opens in a new tab)
Q3_K_M 45.4 GB 96.0 GB 474 tok/s Runs well 82 ptsGreat (A) · 82 of 100 points: Model quality 31.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.9/15 GGUF files (opens in a new tab)
Q4_K_M 55.2 GB 96.0 GB 367 tok/s Runs well 89 ptsExcellent (S) · 89 of 100 points: Model quality 35.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.3/15 GGUF (opens in a new tab)
Q5_K_M 63.3 GB 96.0 GB 303 tok/s Runs well 91 ptsExcellent (S) · 91 of 100 points: Model quality 36.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 72.3 GB 96.0 GB 254 tok/s Runs well 92 ptsExcellent (S) · 92 of 100 points: Model quality 37.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 91.8 GB 96.0 GB 187 tok/s Tight fit 91 ptsExcellent (S) · 91 of 100 points: Model quality 37.8/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 168 GB 96.0 GB — Won't run 11 ptsWon't run (F) · 11 of 100 points: Model quality 11.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.