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Can the Apple M2 Pro (32 GB) run Llama 2 13B Chat?

58 ptsGood (B) · 58 of 100 points: Model quality 9.8/45, Memory fit 25.0/25, Speed 10.9/15, Memory use 13.0/15

Yes, it runs well

Runs well. At Q3_K_M it needs about 12.1 GB of 21.4 GB usable, with an estimated speed of 21.0 tok/s.

Best quant
Q3_K_M
Generation speed
21.0 tok/s
Prompt processing
324 tok/s
First token (1K prompt)
3.2 s
Largest safe context
4K

Download Llama 2 13B Chat

Recommended for the Apple M2 Pro (32 GB): Q3_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 llama2:13b-chat-q3_K_M

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 Q3_K_M file (opens in a new tab)

huggingface.co/TheBloke/Llama-2-13B-chat-GGUF (opens in a new tab)

ollama run hf.co/TheBloke/Llama-2-13B-chat-GGUF:Q3_K_M

Original weights · Hugging Face

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

huggingface.co/meta-llama/Llama-2-13b-chat-hf (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Meta approves it (usually within minutes to a few hours).

Official repo · GitHub

Reference code, inference scripts and docs from Meta. The weights themselves download from Hugging Face.

github.com/meta-llama/llama (opens in a new tab)

git clone https://github.com/meta-llama/llama

Meta’s original Llama 2 repo (now archived).

Links checked 6 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.9.8 / 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.10.9 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.13.0 / 15
Total58.7 / 10058 ptsGood (B) · 58 of 100 points: Model quality 9.8/45, Memory fit 25.0/25, Speed 10.9/15, Memory use 13.0/15

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 10.9 GB 21.4 GB 26.7 tok/s Runs well 57 ptsUsable (C) · 57 of 100 points: Model quality 8.2/45, Memory fit 25.0/25, Speed 12.2/15, Memory use 11.7/15 GGUF (opens in a new tab)
Q3_K_M 12.1 GB 21.4 GB 21.0 tok/s Runs well 58 ptsGood (B) · 58 of 100 points: Model quality 9.8/45, Memory fit 25.0/25, Speed 10.9/15, Memory use 13.0/15 GGUF (opens in a new tab)
Q4_K_M 13.6 GB 21.4 GB 16.2 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 11.1/45, Memory fit 25.0/25, Speed 9.6/15, Memory use 14.7/15 GGUF (opens in a new tab)
Q5_K_M 14.9 GB 21.4 GB 13.4 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 11.3/45, Memory fit 25.0/25, Speed 8.7/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 16.3 GB 21.4 GB 11.3 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 11.6/45, Memory fit 25.0/25, Speed 8.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 19.5 GB 21.4 GB 8.3 tok/s Tight fit 57 ptsUsable (C) · 57 of 100 points: Model quality 11.7/45, Memory fit 24.0/25, Speed 6.8/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 31.7 GB 21.4 GB — Won't run 3 ptsWon't run (F) · 3 of 100 points: Model quality 3.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.