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Can the Apple M4 Max (64 GB) run Qwen3.6 35B-A3B?

96 ptsExcellent (S) · 96 of 100 points: Model quality 41.7/45, 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 34.1 GB of 48.0 GB usable, with an estimated speed of 66.7 tok/s.

Best quant
Q6_K
Generation speed
66.7 tok/s
Prompt processing
3,803 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Download Qwen3.6 35B-A3B

Recommended for the Apple M4 Max (64 GB): 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 qwen3.6:35b

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/Qwen3.6-35B-A3B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen3.6-35B-A3B-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3.6-35B-A3B (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/Qwen3.8 (opens in a new tab)

git clone https://github.com/QwenLM/Qwen3.8

Qwen’s current repo: Qwen3.5 and Qwen3.6 redirect here.

Links checked 5 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.41.7 / 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
Total96.7 / 10096 ptsExcellent (S) · 96 of 100 points: Model quality 41.7/45, 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 M4 Max (64 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 19.0 GB 48.0 GB 158 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 29.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.1/15 GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M 22.2 GB 48.0 GB 124 tok/s Runs well 86 ptsExcellent (S) · 86 of 100 points: Model quality 35.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.7/15 GGUF (opens in a new tab)
Q4_K_M 26.5 GB 48.0 GB 96.2 tok/s Runs well 92 ptsExcellent (S) · 92 of 100 points: Model quality 40.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.8/15 GGUF (opens in a new tab)
Q5_K_M 30.1 GB 48.0 GB 79.5 tok/s Runs well 95 ptsExcellent (S) · 95 of 100 points: Model quality 40.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.5/15 GGUF (opens in a new tab)
Q6_K 34.1 GB 48.0 GB 66.7 tok/s Runs well 96 ptsExcellent (S) · 96 of 100 points: Model quality 41.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 42.7 GB 48.0 GB 49.0 tok/s Tight fit 96 ptsExcellent (S) · 96 of 100 points: Model quality 42.1/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 76.5 GB 48.0 GB — Won't run 12 ptsWon't run (F) · 12 of 100 points: Model quality 12.6/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.