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Can the Apple M2 Pro (32 GB) run Phi-3 Mini 4K Instruct?

63 ptsGood (B) · 63 of 100 points: Model quality 16.0/45, Memory fit 25.0/25, Speed 14.7/15, Memory use 7.6/15

Yes, it runs well

Runs well. At Q6_K it needs about 7.1 GB of 21.4 GB usable, with an estimated speed of 38.4 tok/s.

Best quant
Q6_K
Generation speed
38.4 tok/s
Prompt processing
1,104 tok/s
First token (1K prompt)
0.9 s
Largest safe context
4K

Download Phi-3 Mini 4K 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 phi3:3.8b-mini-4k-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.

huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf (opens in a new tab)

ollama run hf.co/microsoft/Phi-3-mini-4k-instruct-gguf

Original weights · Hugging Face

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

huggingface.co/microsoft/Phi-3-mini-4k-instruct (opens in a new tab)

No sign-in needed.

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.16.0 / 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.14.7 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.7.6 / 15
Total63.3 / 10063 ptsGood (B) · 63 of 100 points: Model quality 16.0/45, Memory fit 25.0/25, Speed 14.7/15, Memory use 7.6/15

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 5.5 GB 21.4 GB 90.9 tok/s Runs well 57 ptsUsable (C) · 57 of 100 points: Model quality 11.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.9/15
Q3_K_M 5.8 GB 21.4 GB 71.5 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 13.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.2/15
Q4_K_M 6.3 GB 21.4 GB 55.3 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 15.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.7/15
Q5_K_M 6.6 GB 21.4 GB 45.7 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 15.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.1/15
Q6_K 7.1 GB 21.4 GB 38.4 tok/s Runs well 63 ptsGood (B) · 63 of 100 points: Model quality 16.0/45, Memory fit 25.0/25, Speed 14.7/15, Memory use 7.6/15
Q8_0 8.0 GB 21.4 GB 28.2 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 16.2/45, Memory fit 25.0/25, Speed 12.6/15, Memory use 8.6/15
F16 11.6 GB 21.4 GB 13.0 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 16.2/45, Memory fit 25.0/25, Speed 8.5/15, Memory use 12.4/15 GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 4K 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.