"FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving"

FlashInfer makes LLM serving faster by smartly managing memory and adapting to different workloads.

FlashInfer introduces a unified attention engine that optimizes LLM serving through customizable templates, block-sparse storage, and dynamic scheduling, significantly improving inference performance.

29-69% inter-token-latency reduction vs Triton backend

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https://arxiv.org/abs/2501.01005

🤔 Original Problem:

LLM serving faces challenges with diverse workload patterns, varying input lengths, and hardware-specific optimizations. Current solutions implement specialized attention mechanisms, leading to maintenance overhead and potential inefficiencies.

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🔧 Solution in this Paper:

→ FlashInfer uses block-sparse format to handle KV-cache storage heterogeneity, enabling efficient memory access and reduced redundancy

→ A customizable attention template supports various attention variants through JIT compilation, allowing rapid adaptation to different configurations

→ Dynamic load-balanced scheduling algorithm adjusts to input changes while maintaining CUDAGraph compatibility

→ Integration with major serving frameworks like SGLang, vLLM and MLC-Engine ensures practical applicability

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🎯 Key Insights:

→ Block-sparse format with flexible block sizes effectively unifies diverse KV-cache patterns

→ JIT compilation enables customization without performance overhead

→ Load-balanced scheduling crucial for handling variable sequence lengths

→ Composable formats improve memory efficiency for shared prefixes

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📊 Results:

→ 29-69% inter-token-latency reduction vs Triton backend

→ 28-30% latency reduction for long-context inference

→ 13-17% speedup for parallel generation

→ Significant bandwidth and FLOPs utilization improvements