LServe introduces a novel system to dramatically improve long-sequence LLM performance by addressing the two major bottlenecks: attention computation and KV cache memory usage.

• Unifies sparse attention approaches for both prefilling and decoding stages
• Achieves up to 2.3× latency reduction and 5.8× throughput improvement
• Maintains high accuracy while significantly reducing memory footprint
• Demonstrates practical implementation across different LLM architectures

This research resolves a critical engineering challenge by making long-context LLMs more feasible for real-world applications, enabling more efficient text generation while preserving quality.

LServe: Efficient Long-sequence LLM Serving with Unified Sparse Attention