Can the Apple M2 Pro (16 GB) run CogVideoX 2B?

62 pts

Yes, it runs well

Runs well. At FP16 it needs about 7.9 GB of 10.7 GB usable, with an estimated speed of 13.0 min.

Best precision: FP16
Memory mode: Text encoder offloaded
Time per clip: 13.0 min
Output: 720×480, 49 frames, 50 steps

Download CogVideoX 2B

Original weights · Hugging Face

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

huggingface.co/zai-org/CogVideoX-2b (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/CogVideo (opens in a new tab)

git clone https://github.com/zai-org/CogVideo

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.	20.3 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.	20.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	7.2 / 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	62.5 / 100	62 ptsGood (B) · 62 of 100 points: Model quality 20.3/45, Memory fit 20.0/25, Speed 7.2/15, Memory use 15.0/15

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

Every precision on the Apple M2 Pro (16 GB)

Precision	Memory needed	Available	Time per output	Memory mode	Fit	Grade · points	Download
`FP16`	7.9 GB	10.7 GB	13.0 min	Text encoder offloaded	Runs well	62 ptsGood (B) · 62 of 100 points: Model quality 20.3/45, Memory fit 20.0/25, Speed 7.2/15, Memory use 15.0/15	Original weights (opens in a new tab)
`FP8`	6.2 GB	10.7 GB	11.7 min	Text encoder offloaded	Runs well	60 ptsGood (B) · 60 of 100 points: Model quality 19.8/45, Memory fit 20.0/25, Speed 7.6/15, Memory use 13.4/15	Original weights (opens in a new tab)

Other hardware that runs it

75 ptsGreat (A) · 75 of 100 points: Model quality 20.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX FP16 · 67 s
75 ptsGreat (A) · 75 of 100 points: Model quality 20.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 3090 FP16 · 1.9 min
75 ptsGreat (A) · 75 of 100 points: Model quality 20.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4090 FP16 · 60 s
74 ptsGreat (A) · 74 of 100 points: Model quality 19.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 AMD Radeon RX 9070 XT FP8 · 72 s
74 ptsGreat (A) · 74 of 100 points: Model quality 19.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4060 Ti 16GB FP8 · 2.4 min
74 ptsGreat (A) · 74 of 100 points: Model quality 19.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4070 Ti SUPER FP8 · 86 s

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.