What GPU do I need to run zai-org/CogVideoX-2b?
1.7B parameters, published in F16. View on Hugging Face
Set up CogVideoX-2bCogVideoX-2b is published by zai-org on Hugging Face, with 20,951 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP16 | 3.2 GB | 3.8 GB | P4 | 1 | $0.069/hr |
| FP8 (quantized) | 1.6 GB | 1.9 GB | RTX 4000 SFF Ada | 1 | $0.198/hr |
| INT4 (quantized) | 0.8 GB | 0.9 GB | RTX 3060 | 1 | $0.110/hr |
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 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, at $0.069/hr per GPU ($0.069/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
CogVideoX-2b: common questions
How much VRAM does CogVideoX-2b need?
3.8 GB at FP16, 1.9 GB at FP8 (quantized), 0.9 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 3.2 GB of weights plus inference overhead is the whole requirement.
How many copies of CogVideoX-2b fit on one P4?
2, by VRAM alone. That card carries 8.0 GB and one copy needs 3.8 GB at FP16, on a live rate of $0.069/hr for the whole card. Throughput is not modelled here, so 2 copies is not 2 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More CogVideoX models
- CogVideoX-5b (5.6B, BF16)
- CogVideoX-5b-I2V (5.6B, BF16)
Alternatives at this size
Other models for text-to-video within about a third of CogVideoX-2b's 1.7B parameters, from other model lines.
- Wan2.1-T2V-1.3B-Diffusers (1.4B, F32)
- text-to-video-ms-1.7b (1.4B, F32)
- LTX-Video-0.9.5 (1.9B, BF16)
- i2vgen-xl (1.4B, F32)
More on CogVideoX-2b
Fits on an 8 GB GPU at FP16: every model that fits in 8 GB.
Best video generation models: how CogVideoX-2b ranks against the rest.
Related reading: H100 pricing and specs, Fast diffusion inference on GPU VMs, and What AI inference is.
