GLM-5 (744B-A40B) on Apple M3 Ultra (512 GB)

Can the Apple M3 Ultra (512 GB) run GLM-5 (744B-A40B)?

78 pts

Yes, with little memory to spare

Tight fit. At Q2_K it needs about 334 GB of 384 GB usable, with an estimated speed of 17.8 tok/s.

Best quant: Q2_K
Generation speed: 17.8 tok/s
Prompt processing: 440 tok/s
First token (1K prompt): 2.3 s
Largest safe context: 198K

Download GLM-5 (744B-A40B)

Recommended for the Apple M3 Ultra (512 GB): Q2_K. Every other quantization has its own download link in the table below.

Ollama

Ollama only offered this as a cloud model, and has since retired it. To run it locally, use the GGUF files below with llama.cpp or LM Studio.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q2_K files (opens in a new tab)

huggingface.co/unsloth/GLM-5-GGUF (opens in a new tab)

ollama run hf.co/unsloth/GLM-5-GGUF

Original weights · Hugging Face

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

huggingface.co/zai-org/GLM-5 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/zai-org/GLM-5 (opens in a new tab)

git clone https://github.com/zai-org/GLM-5

Links checked 18 hours 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.	29.8 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.	24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	10.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	15.0 / 15
Total	78.8 / 100	78 ptsGreat (A) · 78 of 100 points: Model quality 29.8/45, Memory fit 24.0/25, Speed 10.0/15, Memory use 15.0/15

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`	334 GB	384 GB	17.8 tok/s	Tight fit	78 ptsGreat (A) · 78 of 100 points: Model quality 29.8/45, Memory fit 24.0/25, Speed 10.0/15, Memory use 15.0/15	GGUF · folder (opens in a new tab)
`Q3_K_M`	401 GB	384 GB	11.9 tok/s	Runs with offload	68 ptsGood (B) · 68 of 100 points: Model quality 35.8/45, Memory fit 10.0/25, Speed 8.2/15, Memory use 15.0/15	GGUF · folder (opens in a new tab)
`Q4_K_M`	492 GB	384 GB	—	Won't run	12 ptsWon't run (F) · 12 of 100 points: Model quality 12.1/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q5_K_M`	567 GB	384 GB	—	Won't run	12 ptsWon't run (F) · 12 of 100 points: Model quality 12.4/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q6_K`	650 GB	384 GB	—	Won't run	12 ptsWon't run (F) · 12 of 100 points: Model quality 12.7/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`Q8_0`	831 GB	384 GB	—	Won't run	12 ptsWon't run (F) · 12 of 100 points: Model quality 12.8/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (opens in a new tab)
`F16`	1,540 GB	384 GB	—	Won't run	12 ptsWon't run (F) · 12 of 100 points: Model quality 12.8/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (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.