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Can the Apple M5 Max (64 GB) run GLM-4.1V 9B Thinking?

73 ptsGreat (A) · 73 of 100 points: Model quality 26.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.4/15

Yes, it runs well

Runs well. At Q6_K it needs about 13.2 GB of 48.0 GB usable, with an estimated speed of 43.8 tok/s.

Best quant
Q6_K
Generation speed
43.8 tok/s
Prompt processing
1,139 tok/s
First token (1K prompt)
0.9 s
Largest safe context
64K

Download GLM-4.1V 9B Thinking

Recommended for the Apple M5 Max (64 GB): Q6_K. Every other quantization has its own download link in the table below.

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/unsloth/GLM-4.1V-9B-Thinking-GGUF (opens in a new tab)

ollama run hf.co/unsloth/GLM-4.1V-9B-Thinking-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/zai-org/GLM-4.1V-9B-Thinking (opens in a new tab)

No sign-in needed.

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How the A 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.26.7 / 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.6.4 / 15
Total73.1 / 10073 ptsGreat (A) · 73 of 100 points: Model quality 26.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.4/15

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 8.9 GB 48.0 GB 104 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 4.3/15 GGUF (opens in a new tab)
Q3_K_M 9.8 GB 48.0 GB 81.5 tok/s Runs well 67 ptsGood (B) · 67 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.7/15 GGUF (opens in a new tab)
Q4_K_M 11.1 GB 48.0 GB 63.1 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 25.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.3/15 GGUF (opens in a new tab)
Q5_K_M 12.1 GB 48.0 GB 52.1 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 26.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.8/15 GGUF (opens in a new tab)
Q6_K 13.2 GB 48.0 GB 43.8 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 26.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.4/15 GGUF (opens in a new tab)
Q8_0 15.7 GB 48.0 GB 32.1 tok/s Runs well 72 ptsGreat (A) · 72 of 100 points: Model quality 27.0/45, Memory fit 25.0/25, Speed 13.4/15, Memory use 7.5/15 GGUF (opens in a new tab)
F16 25.4 GB 48.0 GB 14.8 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 27.0/45, Memory fit 25.0/25, Speed 9.1/15, Memory use 12.2/15 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.