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 vibe code yet another one-off load generator. All of these paths have the same problem: 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 saturate a real server without becoming the bottleneck, produce output you can act on, and take five minutes to configure, not five hours. That’s NVIDIA AIPerf.

What AIPerf does differently

AIPerf is the designated successor to GenAI-Perf and is a ground-up rewrite. The design choices reflect hard lessons from running LLM benchmarks at scale:

A clean break from the old architecture.…