Routing in LLM inference is the difference between scaling and stalling

Running more than one vLLM replica in distributed inference workloads significantly impacts performance, particularly when considering routing strategies. A detailed experiment comparing cache-aware routing with default Kubernetes routing demonstrated a dramatic reduction in average inference step time, total runtime, and P95 latency when cache-aware routing was implemented. This improvement is attributed to better execution locality, allowing for the reuse of cached data and reducing the need for repeated prefills. While default Kubernetes routing does not consider request content or model state, the cache-aware approach preferentially routes requests to replicas that are more likely to have relevant context cached, enhancing efficiency. The experiment highlighted that scaling replicas without appropriate routing strategies can degrade performance due to cache fragmentation. Cache behavior, specifically KV cache reuse, emerged as the primary driver of performance differences, with cache-aware routing facilitating significantly higher cache usage. Although single-replica scenarios do not benefit from routing strategies, the role of routing becomes critical in distributed setups, where preserving locality across replicas is crucial for performance improvement. The study underscores the importance of routing in managing stateful workloads and suggests that while cache-aware routing is beneficial, further considerations such as memory pressure and cache eviction should be incorporated for optimal performance, which will be explored in future analyses.

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