Towards Efficient Serving of Network-intensive LLM Inferences
Prefix caching has become a key technique for LLM serving, and nowadays the reusable KVCache contents are often hosted on distributed servers. For long-context LLM inferences with high cache hit ratio, cross-server KVCache transmission has become an emerging performance bottleneck; such network-intensive LLM inferences are increasingly prevalent in the coming era of agentic AI. However, existing LLM inference engines are essentially compute-centric; we find that they are highly inefficient when serving such workloads due to compute-stage service blocking and ignorance of KVCache-transfer cost. To efficiently serve network-intensive LLM inferences, in this paper, we design Sanic, an optimized LLM engine that treats KVCache transmission as a first-class citizen. Viewing KVCache loading and computation as equally-significant stages, Sanic decouples their service control and allows each stage to progress autonomously in an asynchronous manner, thereby improving the overall resource utilization. Moreover, when scheduling competing LLM inferences, Sanic treats the KVCache loading delay as an independent factor in service cost modeling, which is more accurate and can yield better scheduling decisions. Our testbed experiments with diverse benchmarks show that, Sanic can substantially enhance the service efficiency of network-intensive LLM inferences, improving the SLO-attainment by up to 61.67%.