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Can the Apple M3 Ultra (512 GB) run Qwen2 72B Instruct?

65 ptsGood (B) · 65 of 100 points: Model quality 27.4/45, Memory fit 25.0/25, Speed 8.2/15, Memory use 4.7/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 78.4 GB of 384 GB usable, with an estimated speed of 11.9 tok/s.

Best quant
Q4_K_M
Generation speed
11.9 tok/s
Prompt processing
242 tok/s
First token (1K prompt)
4.2 s
Largest safe context
32K

Download Qwen2 72B Instruct

Recommended for the Apple M3 Ultra (512 GB): Q4_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 qwen2:72b-instruct-q4_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 Q4_K_M file (opens in a new tab)

huggingface.co/Qwen/Qwen2-72B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/Qwen/Qwen2-72B-Instruct-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/Qwen/Qwen2-72B-Instruct (opens in a new tab)

No sign-in needed.

Links checked 5 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.27.4 / 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.8.2 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.4.7 / 15
Total65.3 / 10065 ptsGood (B) · 65 of 100 points: Model quality 27.4/45, Memory fit 25.0/25, Speed 8.2/15, Memory use 4.7/15

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

Every quantization on the Apple M3 Ultra (512 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 63.1 GB 384 GB 19.6 tok/s Runs well 59 ptsGood (B) · 59 of 100 points: Model quality 20.2/45, Memory fit 25.0/25, Speed 10.5/15, Memory use 3.8/15 GGUF (opens in a new tab)
Q3_K_M 69.6 GB 384 GB 15.4 tok/s Runs well 62 ptsGood (B) · 62 of 100 points: Model quality 24.2/45, Memory fit 25.0/25, Speed 9.3/15, Memory use 4.2/15 GGUF (opens in a new tab)
Q4_K_M 78.4 GB 384 GB 11.9 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 27.4/45, Memory fit 25.0/25, Speed 8.2/15, Memory use 4.7/15 GGUF (opens in a new tab)
Q5_K_M 85.6 GB 384 GB 9.8 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 27.9/45, Memory fit 25.0/25, Speed 7.4/15, Memory use 5.1/15 GGUF · part 1 of 2 (opens in a new tab)
Q6_K 93.6 GB 384 GB 8.3 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 28.5/45, Memory fit 25.0/25, Speed 6.8/15, Memory use 5.6/15 GGUF · part 1 of 2 (opens in a new tab)
Q8_0 111 GB 384 GB 6.1 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 28.8/45, Memory fit 25.0/25, Speed 5.8/15, Memory use 6.7/15 GGUF · part 1 of 2 (opens in a new tab)
F16 179 GB 384 GB 2.8 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 28.8/45, Memory fit 25.0/25, Speed 4.0/15, Memory use 10.8/15 GGUF · part 1 of 4 (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.