DATASHEET // MAGI-1
Magi-1
Autoregressive video diffusion at 24 GB.
OPEN SOURCE24–48 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.
[ EDITORIAL PICK ]
Why we recommend Magi-1
- Open source
Source is public — you can audit it, fork it, and you'll never lose access to your workflows if Magi-1 the company changes direction.
- Top-tier pick
Power-user score 86/100 — consistently rated highly by people who use this every day, not just benchmark chasers.
- 2 quant formats
Supports FP8, BF16 — you can dial VRAM use up or down to match your card.
[ EVIDENCE NOTE ]
Documentation-led datasheet
This page summarizes upstream documentation, release information, and editorially reviewed catalogue fields. It is not presented as a hands-on benchmark. Verify changing requirements at the official project; report stale data through our corrections channel.
AT-A-GLANCE SIGNALS //
DERIVED FROM THIS PAGE'S DATA- Install difficultyStandard
A standard local install — download, install dependencies, point at your GPU.
- Hardware comfortEnthusiast
Needs 24 GB minimum — RTX 3090 / 4090 territory.
- EcosystemOpen source
Source is public — auditable and forkable, no vendor lock.
- VerificationRecent
Catalogue entry last updated 58 days ago — re-verification due soon.
[ MORE IN THIS NICHE ]
Other heavy video generation tools we rate
Three picks across different tradeoffs — so you don't end up with three near-clones of 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
