Our mission is to advance AI to benefit everyone. @GagaAI_official & @magiai_hq are out now.

Joined March 2025
🪄 MAGI-2 Preview: The Open-Source 100B+ Ultra-Fine-Grained MoE Video Generation Model 📄 Tech Report: sand.ai/blog/magi-2-preview 💻 GitHub: github.com/SandAI-org/MAGI-2… 🏆 114B total parameters, with only 6B activated per token for top-tier performance 🔓 100% open-source & tech report 💰 1/10 the generation cost of mainstream models ⏳ 1/7
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Thanks for making it all the way here. 🙌 New coordinates set. Sand.ai is now live. The frontier is everywhere. Drop by often—more signals are on the way. 🪄 ⏳ 7/7
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🪄 Introducing MagiCompiler: Break the Boundaries of Local Compilation for Large Models. MagiCompiler is a unified compilation framework advancing torch.compile into a global "Compiler as Manager", conquering the VRAM and compute walls for training & multi-modality inference. - Repo: github.com/SandAI-org/MagiCo…
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🌟 Highlights: - Whole-Graph & FSDP-Aware Compilation: Breaks local boundaries. Full-graph capture for inference & layer-wise compilation for FSDP training to maximize aggressive cross-op fusion. - Heuristic Recompute: No more manual checkpointing. Auto-preserves compute-bound ops (e.g., Attention) and recomputes memory-bound ones to slash peak VRAM seamlessly. - JIT Extreme Offloading: Smart Compute + H2D dual-stream overlap. Keeps high "cost-performance" weights in VRAM and prefetches the rest at the last millisecond to eliminate pipeline bubbles. - Plug-and-Play: Just 2 decorators (@magi_compile & @magi_register_custom_op) deliver system-level optimizations without complex model refactoring.
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🪄 Introducing MagiAttention v1.1.0: Defining the Performance Ceiling for Hopper & Blackwell Architectures. MagiAttention is a distributed attention engine sharing the bottlenecks of ultra-long context and heterogeneous masks. - Repo: github.com/SandAI-org/MagiAt… - Blog: sandai-org.github.io/MagiAtt…
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🌟 Highlights: - Blackwell Support: Early Blackwell support via FFA_FA4 backend, leveraging HSTU function & R2P optimizations for next-gen hardware. - Native Group Collective: DeepEP-inspired fused kernels (GroupCast/GroupReduce) to break RDMA bottlenecks and achieve zero-redundancy communication. - Mask-Agnostic SOTA: Constant high-throughput performance even for irregular, complex masks. No more OOM or performance drops in "hard mode." - System-Level Synergy: Dispatch Solver + Adaptive Overlap ensures near-linear scalability on H100 & B200 clusters.
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🪄 Introducing daVinci-MagiHuman: The Performance-Level Audio-Video Generative Foundation Model Proudly open-sourced and jointly developed by SII GAIR Lab & Sand.ai, it sets a new standard for multimodal AI. ⏳ 1/6
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📊 Performance & Benchmarks: Beating Open-Source SOTA Great architecture is nothing without results. We benchmarked daVinci-MagiHuman against leading models like LTX-2.3, Ovi 1.1, and MoVA. The results speak for themselves: 👥 Human Blind Evals: Tested on a 100-sample dataset across multiple dimensions, achieving a massive 70.5% overall win rate! 📈 Objective Metrics: 🎬 Video (VideoScore2): Outperforms LTX-2.3 in Visual Quality & Text Alignment; superior to Ovi 1.1 across the board. 🎙️ Audio (TalkVid-Bench): Crushes the competition with vastly lower Word Error Rates (WER) for crystal-clear, accurate speech. ⏳ 5/6
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Thanks for reading! 🙌 Come visit us at Sand.ai to see what else we're building. We are incredibly proud to contribute to the open-source community and will keep pushing the boundaries of AI! ⏳ 6/6
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The full version of Gaga-1 is here. 🎙️ Voice Reference 📱 Both 16:9 & 9:16 Video ✨ 1080p Export Free for all. Create now.
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GAGA-1 is out now. Free for all. No invite needed.
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GAGA-1 is coming. The AI actor with voice and visuals as one. 🎭 Hollywood-level emotion in every frame 🎬 One-click audio and video sync 🌍 Perfect performance in multiple languages 👇 Here are 9 wild examples:
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