Load Testing LLMs: Tools, Metrics & Realistic Traffic Simulation (2026)

Load testing for Language Learning Models (LLMs) differs significantly from traditional API load testing due to the complexities of streaming responses, variable-length outputs, and unique performance characteristics such as GPU saturation and token-level metrics. Key metrics for LLMs include Time to First Token (TTFT), Inter-Token Latency (ITL), and End-to-End Latency (E2EL), which are crucial for evaluating user-perceived responsiveness and overall system performance. Various tools like LLMPerf, NVIDIA GenAI-Perf, GuideLLM, k6, and Locust with LLM extensions are available for conducting LLM load tests, each with its strengths and limitations. These tools help simulate real-world traffic, identify bottlenecks such as GPU saturation, KV cache pressure, and queue depth, and ensure that systems meet their defined Service Level Objectives (SLOs). The document emphasizes the importance of designing realistic test scenarios that reflect production environments, including diverse prompt distributions, concurrency patterns, and streaming dynamics, while also highlighting the need for continuous monitoring post-deployment to maintain optimal performance and identify any drift over time.