CogVideoX 2B on NVIDIA Jetson Orin Nano Super (8 GB)

Can the NVIDIA Jetson Orin Nano Super (8 GB) run CogVideoX 2B?

52 pts

Yes, it runs well

Runs well. At FP8 it needs about 4.7 GB of 6.0 GB usable, with an estimated speed of 18.4 min.

Best precision: FP8
Memory mode: Sequential CPU offload
Time per clip: 18.4 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 5 days ago against Hugging Face, Ollama and GitHub.

How the C 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.	19.8 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.	12.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	6.1 / 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	52.9 / 100	52 ptsUsable (C) · 52 of 100 points: Model quality 19.8/45, Memory fit 12.0/25, Speed 6.1/15, 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 NVIDIA Jetson Orin Nano Super (8 GB)

Precision	Memory needed	Available	Time per output	Memory mode	Fit	Grade · points	Download
`FP16`	5.3 GB	6.0 GB	20.4 min	Sequential CPU offload	Tight fit	50 ptsUsable (C) · 50 of 100 points: Model quality 20.3/45, Memory fit 10.0/25, Speed 5.7/15, Memory use 15.0/15	Original weights (opens in a new tab)
`FP8`	4.7 GB	6.0 GB	18.4 min	Sequential CPU offload	Runs well	52 ptsUsable (C) · 52 of 100 points: Model quality 19.8/45, Memory fit 12.0/25, Speed 6.1/15, Memory use 15.0/15	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.