CogVideoX-5B
Open-source 3D video diffusion transformer with 3D VAE and expert transformer blocks, generating 720p 6-second cinematic video clips under Apache 2.0.
The brutal truth
A landmark in open video generation. Wan 2.2 is easier on low VRAM, but CogVideoX-5B has exceptional motion fluidity on 24GB rigs.
Files and Versions
Direct inspected tree artifacts from Hugging Face. Verified filenames, exact byte sizes, model weight shards, and configuration definitions.
THUDM/CogVideoX-5bFiles and versions are hosted on Hugging Face. You can view all branches, commits, model weights, and tokenizer artifacts directly.
Inspect THUDM/CogVideoX-5b /tree/main↗Decision matrix
✓ RUN / USE THIS IF…
- •You have a 16GB or 24GB GPU
- •You want 6-second cinematic motion clips
- •Your 24GB GPU comfortably fits the model weights (~12GB VRAM) with dedicated headroom for inference.
- •You require 100% offline data privacy with zero third-party API logging, subscriptions, or prompt retention.
✕ WALK AWAY IF…
- •You are on an 8GB GPU (use Wan 2.2 instead)
- •Your GPU has less than 12GB VRAM and you cannot tolerate slow CPU/system RAM offload paging.
- •You prefer a plug-and-play web app interface and don't want to manage local model checkpoints, drivers, or runtimes.
- •You need specialized frontier reasoning that exceeds this model's parameter envelope.
True cost of ownership
About
Open-source 3D video diffusion transformer with 3D VAE and expert transformer blocks, generating 720p 6-second cinematic video clips under Apache 2.0.
What it does
Download the weights from Hugging Face, pick a quantisation that matches your GPU, and run it locally with Ollama, llama.cpp, vLLM, or a compatible runtime. Temporal motion consistency & Apache 2.0 video runs on your own hardware — no per-token bill, no third-party data handling.
Key features
Coherent fluid motion over 6-second video duration
3D causal VAE preserves temporal motion consistency
Apache 2.0 license
Use cases
Turn a script into a polished 15–30 second clip.
Visualise shot lists before a full production run.
Fill timeline gaps with convincing atmospheric footage.
Create stylised clips from a lyric sheet or mood board.
Quantization & Hardware Fit
VRAM Hardware Compatibility & Memory Allocation
| Quant | VRAM | Quality / Fit on 24GB | Install / Run Command (COMFYUI) | |
|---|---|---|---|---|
| INT8 GGUF● ACTIVE | 12 GB | Standard · ✓ Fits (12GB spare) | comfy model download --url https://huggingface.co/THUDM/CogVideoX-5b --relative-path models/diffusion_models | |
| BF16 | 22 GB | Standard · ✓ Fits (2GB spare) | comfy model download --url https://huggingface.co/THUDM/CogVideoX-5b --relative-path models/diffusion_models | |
| FP16 | 26 GB | Standard · ✕ Needs 26GB | comfy model download --url https://huggingface.co/THUDM/CogVideoX-5b --relative-path models/diffusion_models |
Buyer doubts, cleared
Can I actually run CogVideoX-5B on my 24GB GPU?
What's the fine print on the license?
Does the Text-to-video / Image-to-video actually hold up in practice?
User reviews
Reasoning quality is close to the bigger models for structured tasks. Open-ended planning still needs a frontier model.
Clean interface, consistent results once you land on a prompt you like. Wish there were more export presets for client handoffs.
Used CogVideoX-5B for a client project. Documentation was solid, though the fine-tuning path is less polished than base inference.
CogVideoX-5B handled our batch jobs without surprises. The API is well documented and the rate limits are reasonable.
