Native sparse attention

A hardware-aligned, end-to-end trainable sparse-attention design that learns which key/value blocks to attend to, cutting compute and memory for both training and inference on very long (million-token) sequences rather than sparsifying a dense model after the fact.

grounded in: latest AI/tech trends: NOTABLE 'Flash-MSA: Sparse-attention kernels that accelerate million-token training runs' + theme 'LLM cost/latency efficiency ... sparse-attention kernels to cut token spend' (

Connected concepts

Sparse attention, FlashAttention (IO-aware attention), Kernel fusion, Memory-bandwidth-bound decode, Context parallelism