<ul><li>Large Language Models (LLMs) face memory inefficiencies during long-context inference.</li><li>A new integration of PagedAttention with PyTorch's FlexAttention is introduced to improve efficiency.</li><li>The fusion of attention kernel in IBM's Foundation Model Stack (FMS) reduces inference latency significantly.</li><li>Benchmarks on an NVIDIA L4 GPU show reduced latency with global KV cache, maintaining linear growth with sequence length.</li></ul>

Paged Attention Meets FlexAttention: Unlocking Long-Context Efficiency in Deployed Inference

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