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Can the Apple M2 Pro (32 GB) run Qwen2.5 3B Instruct?

60 ptsGood (B) · 60 of 100 points: Model quality 15.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.5/15

Yes, it runs well

Runs well. At Q6_K it needs about 5.2 GB of 21.4 GB usable, with an estimated speed of 47.5 tok/s.

Best quant
Q6_K
Generation speed
47.5 tok/s
Prompt processing
1,364 tok/s
First token (1K prompt)
0.8 s
Largest safe context
32K

Download Qwen2.5 3B Instruct

Recommended for the Apple M2 Pro (32 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 qwen2.5:3b-instruct-q6_K

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/Qwen2.5-3B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/Qwen/Qwen2.5-3B-Instruct-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen2.5-3B-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 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.15.1 / 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.5.5 / 15
Total60.7 / 10060 ptsGood (B) · 60 of 100 points: Model quality 15.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.5/15

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 3.9 GB 21.4 GB 112 tok/s Runs well 54 ptsUsable (C) · 54 of 100 points: Model quality 10.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.1/15 GGUF (opens in a new tab)
Q3_K_M 4.1 GB 21.4 GB 88.4 tok/s Runs well 57 ptsUsable (C) · 57 of 100 points: Model quality 12.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.4/15 GGUF (opens in a new tab)
Q4_K_M 4.5 GB 21.4 GB 68.4 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 14.5/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.8/15 GGUF (opens in a new tab)
Q5_K_M 4.8 GB 21.4 GB 56.5 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 14.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.2/15 GGUF (opens in a new tab)
Q6_K 5.2 GB 21.4 GB 47.5 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 15.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.5/15 GGUF (opens in a new tab)
Q8_0 5.9 GB 21.4 GB 34.8 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 15.3/45, Memory fit 25.0/25, Speed 14.0/15, Memory use 6.3/15 GGUF (opens in a new tab)
F16 8.8 GB 21.4 GB 16.1 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 15.3/45, Memory fit 25.0/25, Speed 9.5/15, Memory use 9.5/15 GGUF · part 1 of 2 (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.