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Can the Apple M2 Pro (32 GB) run Qwen3 4B Thinking 2507?

71 ptsGood (B) · 71 of 100 points: Model quality 24.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.9/15

Yes, it runs well

Runs well. At Q5_K_M it needs about 6.4 GB of 21.4 GB usable, with an estimated speed of 43.4 tok/s.

Best quant
Q5_K_M
Generation speed
43.4 tok/s
Prompt processing
1,049 tok/s
First token (1K prompt)
1.0 s
Largest safe context
107K

Download Qwen3 4B Thinking 2507

Recommended for the Apple M2 Pro (32 GB): Q5_K_M. 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 qwen3:4b-thinking

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 Q5_K_M file (opens in a new tab)

huggingface.co/unsloth/Qwen3-4B-Thinking-2507-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Qwen3-4B-Thinking-2507-GGUF:Q5_K_M

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen3-4B-Thinking-2507 (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 1 week ago against Hugging Face, Ollama and GitHub.

How the B 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.24.4 / 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.6.9 / 15
Total71.3 / 10071 ptsGood (B) · 71 of 100 points: Model quality 24.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.9/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the Apple M2 Pro (32 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 5.1 GB 21.4 GB 86.4 tok/s Runs well 63 ptsGood (B) · 63 of 100 points: Model quality 17.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.5/15 GGUF (opens in a new tab)
Q3_K_M 5.5 GB 21.4 GB 68.0 tok/s Runs well 67 ptsGood (B) · 67 of 100 points: Model quality 21.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.9/15 GGUF (opens in a new tab)
Q4_K_M 6.0 GB 21.4 GB 52.6 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 23.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.4/15 GGUF (opens in a new tab)
Q5_K_M 6.4 GB 21.4 GB 43.4 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 24.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.9/15 GGUF (opens in a new tab)
Q6_K 6.8 GB 21.4 GB 36.5 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 24.9/45, Memory fit 25.0/25, Speed 14.3/15, Memory use 7.3/15 GGUF (opens in a new tab)
Q8_0 7.8 GB 21.4 GB 26.8 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 25.2/45, Memory fit 25.0/25, Speed 12.3/15, Memory use 8.4/15 GGUF (opens in a new tab)
F16 11.6 GB 21.4 GB 12.3 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 25.2/45, Memory fit 25.0/25, Speed 8.3/15, Memory use 12.4/15 GGUF (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.