AI BriefingMiniMax Sparse Attention for Million-Token Context (GitHub Repo)
Key point
MiniMax has released MSA, a Sparse Attention technology that supports processing long contexts of over a million tokens.
Details
MSA (MiniMax Sparse Attention), developed by MiniMax, is a technology for efficiently processing ultra-long contexts of over a million tokens. This library targets the NVIDIA SM100 architecture and provides both dense FlashAttention and Sparse Top-k Attention kernels.
The main components are as follows:
- csrc JIT: Dense FMHA and Sparse Top-k Select indexer compiled at runtime via Jinja templates.
- CuTe-DSL: Full Sparse Attention (Forward + Paged FP8 Decode) support compiled at runtime.
- Bridge: Connects the FMHA API to the Sparse Atten Func for the Sparse Prefill path.
Users can use this on a Linux x86_64 system with Python 3.10 or higher, an NVIDIA SM100 GPU, and CUDA Toolkit installed. It can also be conveniently called and used via Hugging Face's kernels library.
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