- Recent measurements by Red Hat have uncovered significant performance discrepancies in large language model (LLM) inference when utilizing different load balancing methods,...
- The core reason behind this performance gap lies in the utilization of the KV-cache.
- This analysis is particularly relevant for engineers deploying self-hosted LLM inference in open-source container orchestration system for automating software deployment, scaling, and management.">Kubernetes across multiple GPUs, as well as for platform owners...
The Critical Role of Load Balancing in LLM Performance
Recent measurements by Red Hat have uncovered significant performance discrepancies in large language model (LLM) inference when utilizing different load balancing methods, despite similar requests per second (RPS) metrics. The study demonstrated that while both traditional round-robin and prefix-aware routing exhibited nearly identical RPS values (0.52 and 0.49 respectively), the p95 time to first token (TTFT) varied drastically. Round-robin showed a p95 TTFT of 745 ms, which is 2.7 times worse than the 272 ms recorded for prefix-aware routing.
KV-Cache: A Key to Efficient Routing
The core reason behind this performance gap lies in the utilization of the KV-cache. In the experiments, the GPUs, model, and overall workload remained constant, with only the replica selection method changing. The standard RPS counter fails to account for the internal processing a pod undertakes for a specific request. If a request is routed to a replica where the necessary prefix is already present in the KV-cache, the model can commence token generation much faster. Conversely, if the same request lands on an adjacent replica without this pre-cached state, it must re-compute the entire query head, leading to substantial delays.
Applicability and Infrastructure Considerations
This analysis is particularly relevant for engineers deploying self-hosted LLM inference in Kubernetes across multiple GPUs, as well as for platform owners who assess workload based on GPU utilization and request counts. For single-replica setups, routing challenges do not arise, but they become critical at scale. All presented examples are based on public measurements from various teams, focusing on the infrastructure impact of routing on GPU usage, latency, and Kubernetes cluster requirements. VK Tech provides GPU nodes and vGPU solutions for deploying such high-performance systems.
While the findings on prefix-aware routing improving TTFT are compelling, I wonder about the practical overhead and complexity of implementing such a sophisticated load balancing mechanism. Does the computational cost of intelligently routing requests based on KV-cache state potentially offset some of these latency gains in extremely high-throughput scenarios, or introduce new points of failure? Also, the article doesn’t delve into the potential difficulties of maintaining cache coherence across a distributed system, which could be a significant challenge.