LLMRunner: Closed Workload Model Performance Testing Framework for LLM Servings
Arush Upadhyaya, Debadatta Mishra
原文摘要
Performance testing large language model (LLM) serving engines is a crucial requirement considering the criticality of the infrastructure for deploying conversational AI at scale. Most of the benchmarking tools used to carryout performance evaluation of LLM serving do not capture realistic workload orchestration unlike closed workload performance testing infrastructure used in other domains such as web and database engines. For example, state-of-the-art LLM benchmarks for performance testing model traffic as streams of independent single-turn and stand-alone requests omitting the end-user behavior such as think-time between conversational turns. We present LLMRunner, a performance tester for LLM serving that addresses this gap by providing a framework to generate realistic load consisting of multi-turn conversations with configurable end-user parameters such as number of users in the workload loop, think-time distributions etc. Moreover, LLMRunner provides declarative matrix-sweep experiment plans, integrated GPU and KV-cache monitoring, and an automated artifact collection and plotting subsystem. We use LLMRunner to comparatively analyze the performance of LLM serving when NVIDIA CUDA Unified Virtual Memory (UVM) is used as a drop-in replacement memory allocator for the Key-Value cache (KV-Cache). Our test demonstrates that UVM incurs no measurable throughput or latency penalty compared to the baseline memory allocation technique for non-oversubscription scenarios.
cs.PF