Optimizing Scalable LLM Inference: System-Level Strategies for Proactive KV Cache Management

As large language models (LLMs) increasingly underpin mission-critical applications across industries, optimizing their inference efficiency has emerged as a critical priority. Central to this optimization is the effective management of the Key-Value (KV) cache, a memory-intensive component that stores intermediate computations to accelerate autoregressive token generation.

 

In this talk, we examine recent advancements in system-level KV cache management, emphasizing novel approaches to proactive scheduling that dynamically allocate computational and memory resources. We evaluate techniques optimized for diverse operational contexts—spanning offline batch processing to real-time online serving—and discuss architectural optimizations for single-instance execution as well as coordination strategies for concurrent multi-instance deployments. Finally, we outline promising research directions to address scalability challenges in multi-instance inference. These advancements are crucial for enabling scalable enterprise solutions as LLMs expand into latency-sensitive, high-throughput industrial applications.