Understanding the Benchmark Challenge

As more organizations deploy large language models (LLMs) on their own infrastructure, understanding how to measure performance has become a common challenge.

Customers often ask: How do I benchmark my self-hosted LLM? Which metrics matter? And what do those numbers actually mean for real workloads? Benchmarking LLMs is not as straightforward as testing traditional AI models. LLMs have billions of parameters and their performance depends on many factors, such as hardware setup, memory bandwidth, quantization, KV-cache behavior, and parallelism strategy. Even small changes in configuration, prompt length, or user behavior can produce large differences in throughput, latency. Production workloads can shift unexpectedly, if users suddenly max out your context length when you weren't testing for it, performance will degrade significantly.

Once you've achieved acceptable response quality for your use case, performance becomes the critical next concern. Can your model respond quickly enough to meet user expectations? And can it scale to handle your actual workload, whether that's ten concurrent users or ten thousand? These questions directly impact user experience, infrastructure costs, and the viability of your deployment.

Without a clear benchmarking framework, teams struggle to compare hardware options, tune models efficiently, or predict deployment costs. This guide focuses on inference performance benchmarking; measuring throughput, latency, and resource utilization to help customers optimize deployment configurations, understand key metrics in practice, and implement a structured approach to analyzing and improving inference performance.