Check my GPU

on

Can the Apple M5 Max (64 GB) run Qwen2.5 Coder 3B Instruct?

61 ptsGood (B) · 61 of 100 points: Model quality 16.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.3/15

Yes, it runs well

Runs well. At F16 it needs about 10.9 GB of 48.0 GB usable, with an estimated speed of 49.3 tok/s.

Best quant
F16
Generation speed
49.3 tok/s
Prompt processing
3,792 tok/s
First token (1K prompt)
0.3 s
Largest safe context
32K

Download Qwen2.5 Coder 3B Instruct

Recommended for the Apple M5 Max (64 GB): F16. 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-coder:3b-instruct-fp16

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 F16 file (opens in a new tab)

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

ollama run hf.co/Qwen/Qwen2.5-Coder-3B-Instruct-GGUF:FP16

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen2.5-Coder-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-Coder (opens in a new tab)

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

Qwen’s coder repo, renamed from Qwen2.5-Coder to Qwen3-Coder.

Links checked 7 days 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.16.2 / 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.5.3 / 15
Total61.5 / 10061 ptsGood (B) · 61 of 100 points: Model quality 16.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.3/15

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

Every quantization on the Apple M5 Max (64 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 6.0 GB 48.0 GB 345 tok/s Runs well 54 ptsUsable (C) · 54 of 100 points: Model quality 11.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 2.9/15 GGUF (opens in a new tab)
Q3_K_M 6.3 GB 48.0 GB 272 tok/s Runs well 56 ptsUsable (C) · 56 of 100 points: Model quality 13.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.0/15 GGUF (opens in a new tab)
Q4_K_M 6.6 GB 48.0 GB 210 tok/s Runs well 58 ptsGood (B) · 58 of 100 points: Model quality 15.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.2/15 GGUF (opens in a new tab)
Q5_K_M 6.9 GB 48.0 GB 174 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 15.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.3/15 GGUF (opens in a new tab)
Q6_K 7.3 GB 48.0 GB 146 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 16.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.5/15 GGUF (opens in a new tab)
Q8_0 8.0 GB 48.0 GB 107 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 16.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 3.9/15 GGUF (opens in a new tab)
F16 10.9 GB 48.0 GB 49.3 tok/s Runs well 61 ptsGood (B) · 61 of 100 points: Model quality 16.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.3/15 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.