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Can the Apple M5 Max (64 GB) run Gemma 2 9B IT?

68 ptsGood (B) · 68 of 100 points: Model quality 22.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.6/15

Yes, it runs well

Runs well. At Q6_K it needs about 13.6 GB of 48.0 GB usable, with an estimated speed of 48.7 tok/s.

Best quant
Q6_K
Generation speed
48.7 tok/s
Prompt processing
1,268 tok/s
First token (1K prompt)
0.8 s
Largest safe context
8K

Download Gemma 2 9B IT

Recommended for the Apple M5 Max (64 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 gemma2:9b-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/bartowski/gemma-2-9b-it-GGUF (opens in a new tab)

ollama run hf.co/bartowski/gemma-2-9b-it-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/google/gemma-2-9b-it (opens in a new tab)

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

Official repo · GitHub

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

github.com/google-deepmind/gemma (opens in a new tab)

git clone https://github.com/google-deepmind/gemma

Google DeepMind’s official Gemma library.

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.22.3 / 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.6.6 / 15
Total68.8 / 10068 ptsGood (B) · 68 of 100 points: Model quality 22.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.6/15

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 9.7 GB 48.0 GB 115 tok/s Runs well 60 ptsGood (B) · 60 of 100 points: Model quality 15.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.7/15 GGUF (opens in a new tab)
Q3_K_M 10.6 GB 48.0 GB 90.8 tok/s Runs well 63 ptsGood (B) · 63 of 100 points: Model quality 18.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.1/15 GGUF (opens in a new tab)
Q4_K_M 11.7 GB 48.0 GB 70.2 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 21.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.6/15 GGUF (opens in a new tab)
Q5_K_M 12.6 GB 48.0 GB 58.0 tok/s Runs well 67 ptsGood (B) · 67 of 100 points: Model quality 21.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.1/15 GGUF (opens in a new tab)
Q6_K 13.6 GB 48.0 GB 48.7 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 22.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.6/15 GGUF (opens in a new tab)
Q8_0 15.8 GB 48.0 GB 35.8 tok/s Runs well 69 ptsGood (B) · 69 of 100 points: Model quality 22.5/45, Memory fit 25.0/25, Speed 14.2/15, Memory use 7.6/15 GGUF (opens in a new tab)
F16 24.5 GB 48.0 GB 16.5 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 22.5/45, Memory fit 25.0/25, Speed 9.6/15, Memory use 11.8/15 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.