Optimize LLM Performance: Efficient Request Queueing with LLM-Server

AI Impact Summary

Serving LLMs in parallel introduces queuing challenges due to GPU resource contention, as demonstrated by 'power users' blocking others. The proposed solution involves a two-stage architecture: an LLM-Server with fair scheduling (round-robin) and a backend inference engine (vLLM). Optimizing this requires monitoring the backend queue length and dynamically adjusting the rate at which requests are sent to vLLM, aiming to minimize latency for new users while avoiding underutilization of GPU resources. This highlights the need for metrics-driven scheduling and prioritization strategies.