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Can the Apple M2 Ultra (192 GB) run Qwen3-Next 80B-A3B Thinking?

93 ptsExcellent (S) · 93 of 100 points: Model quality 38.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q8_0 it needs about 98.5 GB of 144 GB usable, with an estimated speed of 71.7 tok/s.

Best quant
Q8_0
Generation speed
71.7 tok/s
Prompt processing
5,621 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Download Qwen3-Next 80B-A3B Thinking

Recommended for the Apple M2 Ultra (192 GB): Q8_0. 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 Q8_0 file (opens in a new tab)

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

ollama run hf.co/Qwen/Qwen3-Next-80B-A3B-Thinking-GGUF:Q8_0

Original weights · Hugging Face

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

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

No sign-in needed.

Links checked 6 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.38.7 / 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.15.0 / 15
Total93.7 / 10093 ptsExcellent (S) · 93 of 100 points: Model quality 38.7/45, 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 Apple M2 Ultra (192 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 44.8 GB 144 GB 231 tok/s Runs well 74 ptsGreat (A) · 74 of 100 points: Model quality 27.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.2/15 GGUF files (opens in a new tab)
Q3_K_M 52.2 GB 144 GB 182 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 32.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.4/15 GGUF files (opens in a new tab)
Q4_K_M 61.9 GB 144 GB 141 tok/s Runs well 86 ptsExcellent (S) · 86 of 100 points: Model quality 36.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.9/15 GGUF (opens in a new tab)
Q5_K_M 70.1 GB 144 GB 116 tok/s Runs well 88 ptsExcellent (S) · 88 of 100 points: Model quality 37.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 11.2/15 GGUF (opens in a new tab)
Q6_K 79.0 GB 144 GB 97.8 tok/s Runs well 90 ptsExcellent (S) · 90 of 100 points: Model quality 38.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.7/15 GGUF (opens in a new tab)
Q8_0 98.5 GB 144 GB 71.7 tok/s Runs well 93 ptsExcellent (S) · 93 of 100 points: Model quality 38.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 175 GB 144 GB 28.1 tok/s Heavy offload 70 ptsGood (B) · 70 of 100 points: Model quality 38.7/45, Memory fit 4.0/25, Speed 12.6/15, Memory use 15.0/15 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.