Wan 2.2 A14B
A solid pick for self-hosting and full data control.
Overview
Wan 2.2 A14B is Alibaba's open-source text-to-video model, built on a Mixture-of-Experts architecture that splits denoising across specialised experts to raise quality without extra compute cost. It targets cinematic-grade output (lighting, composition, tone) and handles complex motion at 480p/720p. Real limit: inference needs at least 80GB of VRAM, far beyond a typical desktop despite the 'desktop' tier label. Apache 2.0 licence allows full on-premise deployment, keeping data and renders inside the company.
Skill profile
Not disclosed
Strengths
- Open-source and self-hostable
Limitations
- API pricing not disclosed
Who is it for
- you want to control cost or self-host
- you have GDPR constraints
Ideal use cases
- Short marketing videos
- Storyboard prototyping
- Internal ad content
- Video-gen R&D
- Animated product demos
Access & availability
Key specifications
Privacy
Run it locally
| Quantization | Disk | RAM / VRAM | Typical hardware |
|---|---|---|---|
| Q4 · recommended | 15.4 GB | 19 GB | 32GB Mac / RTX 3090-4090 |
| Q8 · balanced | 28.9 GB | 34 GB | 64GB Mac / dual 24GB GPUs |
| FP16 · max quality | 54 GB | 61 GB | 96-128GB Mac Studio / 4× GPUs |
Estimates for a moderate context. Long contexts need more RAM (KV cache).
Deploy
Copy-ready commands generated from this card. Adjust context length and GPU count to your hardware.
OpenAI-compatible server for production on NVIDIA GPUs.
pip install vllm
vllm serve Wan-AI/Wan2.2-T2V-A14B \
--max-model-len 32768 \
--tensor-parallel-size 1 \
--dtype autoAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | — |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |