Sand provides AI infrastructure tools that eliminate compute and memory bottlenecks for large vision and multimodal models. Its Magi video generation model enables real‑time, high‑quality image‑to‑video conversion, while MagiCompiler and MagiAttention automate whole‑graph compilation, operator fusion, and distributed attention to boost speed, reduce VRAM usage, and support ultra‑long contexts without manual tuning.
Funding
Funding not disclosed
Founders
Product
Problem
Developers of large vision and multimodal models face severe compute and memory bottlenecks when training or inference, especially for tasks like image‑to‑video generation and ultra‑long‑context attention. Existing compilers and attention kernels either break the computation graph or require extensive manual tuning, leading to out‑of‑memory errors, low throughput, and high engineering overhead.
Solution
Sand.ai offers a suite of AI infrastructure tools that remove these bottlenecks. The Magi video generation model combines autoregressive modeling with diffusion to enable real‑time, high‑quality image‑to‑video transformation. MagiCompiler provides a unified, “compiler‑as‑manager” framework that captures whole‑graph inference and FSDP‑aware whole‑layer training, automatically handling operator fusion, heuristic recompute, and just‑in‑time weight prefetching to boost speed and reduce VRAM usage. MagiAttention delivers mask‑flexible, load‑balanced distributed attention kernels that scale linearly to multi‑million token contexts while minimizing communication overhead. Together, these technologies let researchers and product teams train and serve large vision models with minimal manual optimization, faster iteration, and lower hardware costs.
Target Audience
Primary customers are AI research labs, enterprise ML teams, and developers building large‑scale vision or multimodal applications that require efficient video generation, long‑context attention, or high‑performance model training.
Features
- Autoregressive‑diffusion video generation (Magi) that creates high‑fidelity video from single images in real time
- MagiCompiler’s whole‑graph capture and FSDP‑aware whole‑layer compilation for both training and inference, delivering up to 45% speedup and 6% VRAM reduction without manual tuning
- Heuristic recompute and JIT prefetch scheduling that automatically balances compute and memory, eliminating pipeline bubbles
- MagiAttention’s Flex‑Flash‑Attention (FFA) kernels supporting arbitrary masks and ultra‑long contexts (4 M+ tokens) with near‑linear scalability on H100 and Blackwell GPUs
- Distributed attention engine with Dispatch Solver load‑balancing, zero‑redundant group collectives, and adaptive multi‑stage overlap to hide communication latency
- Open‑source libraries and API platform (https://platform.sand.ai) with credit‑based usage, API key management, and REST endpoints for generation tasks