Prism Attention: Efficient KV-Cache Sharing for Long-Context LLM Inference
In brief
- Prism Attention cuts memory and compute costs for KV-cache in long-context decoding.
- It dynamically adapts sparse indices across layers to preserve attention accuracy.
- Delivers a 3.4x speedup over FlashAttention at 128K context length.
Prism Attention introduces a dynamic sparse attention mechanism that reduces memory and compute costs for long-context decoding in large language models. By refining cross-layer index sharing, it maintains accuracy while outperforming existing sparse attention methods and achieving a 3.4x speedup over FlashAttention at 128K context length.
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