Benchmarking LLM Performance at Scale with NVIDIA AIPerf
You're deploying a model on a system. It starts up, prompts are getting responses. Now the hard question: Is this fast?
Your instincts might lead you to send curl commands, hand-roll an asyncio script, or write yet another one-off load generator. All these approaches share the same problems: single-process performance limits, Python's GIL capping concurrency, or numbers measured against a reference you built yourself. Either way, you end up with results you can't fully trust, attached to tooling you'll have to rewrite the moment requirements change.
What you need is a load client that can...

