Wan 2.2
Open-weight video diffusion from Alibaba.
Open Source 12–48 GB VRAM
- Min VRAM
- 12 GB
- GPU class
- Workstation GPU
- Quant
- FP16
Actually FreeNo SignupOpen SourceWatermark-Free
Magi-1
Autoregressive video diffusion at 24 GB.
Open Source 24–48 GB VRAMRuns locally
Actually FreeNo SignupOpen SourceWatermark-Free
Visit Magi-1Updated 2026-05-19 · Direct link
Hardware requirements
Runs locally · Workstation GPU (32–48 GB)
Min VRAM
24 GB
Rec. VRAM
48 GB
Min RAM
32 GB
Rec. RAM
64 GB
Disk
80 GB
GPU class
Workstation GPU
12.0+No Apple SiliconGPU RequiredQuant: FP8, BF16
24 GB minimum with FP8; comfortable on 48 GB.
Screenshot placeholder · Magi-1
What is Magi-1?
Magi-1 (Sand AI) is a 24B autoregressive video diffusion model: instead of generating a fixed-length clip in one pass, it produces frames sequentially with kv-cache reuse. Result: arbitrary-length generation and tight prompt consistency across long shots — a different value prop than Wan/Hunyuan.
Pros & cons
Pros
- Arbitrary-length generation, not capped at 5–8 seconds
- Strong temporal consistency through autoregressive sampling
- Apache 2.0 — fully commercial-friendly
Cons
- 24B parameters — 24 GB minimum, 48 GB comfortable
- Autoregressive sampling is slower per second of output than full-clip diffusion
- Younger ecosystem than Wan/Hunyuan
What's actually free?
Apache 2.0; weights free.
✓ Actually FreeNo SignupOpen SourceWatermark-Free
Alternatives
HunyuanVideo
13B open-weight cinematic text-to-video.
Open Source 24–48 GB VRAM
- Min VRAM
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- GPU class
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- Quant
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Actually FreeNo SignupOpen SourceWatermark-Free
LTX-Video
Real-time-ish open video diffusion from Lightricks.
Open Source 12–16 GB VRAM
- Min VRAM
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- GPU class
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- Quant
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Mochi 1
Genmo's 10-B open-weight T2V — the first 'genuinely fluid' OSS video model.
Open Source 24–60 GB VRAM
- Min VRAM
- 24 GB
- GPU class
- Workstation GPU
- Quant
- BF16
Actually FreeNo SignupOpen SourceWatermark-Free