What GPU do I need to run zai-org/CogVideoX-2b?
1.7B parameters, published in F16. View on Hugging Face
CogVideoX-2b is published by zai-org on Hugging Face, with 20,622 downloads and 371 likes to date. It's a unlisted-architecture model built for text-to-video, published natively in F16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for activation memory and allocator fragmentation. Diffusion and video models carry no KV-cache — the real driver of extra memory is output resolution and frame count, which this flat overhead does not model. Full formula and assumptions: methodology.
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 caveat: requires a quantized checkpoint actually published for this model — check its Hugging Face page before relying on this row.
Cheapest way to run CogVideoX-2b at its published (F16) precision: 1× P4 on akash, at $0.032/hr per GPU ($0.032/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More zai-org models
- GLM-OCR (1.3B, BF16)
- GLM-5.2 (753.3B, BF16)
- GLM-4.7-Flash (31.2B, BF16)
- GLM-5.2-FP8 (753.4B, F8_E4M3)
- GLM-5-FP8 (753.9B, F8_E4M3)
- GLM-5.1-FP8 (753.9B, F8_E4M3)
