This research introduces an efficient scheduling framework for LLM inference that significantly reduces computational overhead by leveraging shared prefixes across queries.

• Addresses critical time-to-first-token (TTFT) and time-per-output-token (TPOT) constraints
• Reveals limitations in current first-come-first-serve approaches
• Develops novel algorithms that optimize query processing while meeting strict latency requirements
• Enables more cost-effective LLM deployment in production environments

This engineering advancement matters because it allows organizations to deliver faster LLM responses while utilizing computing resources more efficiently, potentially reducing infrastructure costs for AI services.

LLM Query Scheduling with Prefix Reuse and Latency Constraints