KV Cache Locality: The Hidden Variable in Your LLM Serving Cost (ranvier.systems)

A new post reveals the significant cost implications of KV cache locality in large language model (LLM) serving, outlining that inefficient load balancing can lead to redundant recomputation of prefill tasks across multiple GPUs. For instance, in a scenario using eight GPUs and the CodeLlama 13B model, a round-robin load balancer achieved only a 12.5% cache hit rate, resulting in a slow time-to-first-token (TTFT) of 6,800 milliseconds for concurrent users. In contrast, implementing prefix-aware routing increased the cache hit rate to 97.5%, reducing TTFT to just 1,000 milliseconds and improving throughput by over 22%. This discrepancy represents substantial wasted GPU resources, costing approximately $1,200–$1,800 per month at typical rates. This issue highlights that the efficiency of LLM serving hinges on how well requests are routed to the correct GPUs that already have relevant data cached. The post emphasizes that KV cache locality should not be overlooked, as it serves as a multiplier on hardware capabilities, directly influencing latency and throughput. The introduction of tools like Ranvier aims to address this challenge by using real-time learning algorithms to route requests based on existing cache availability, offering a promising solution to enhance performance and cost-effectiveness in AI/ML applications.