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NVIDIA AIPerf Benchmarks LLM Inference at Scale

NVIDIA has released AIPerf, a new benchmarking tool designed to replace GenAI-Perf, featuring a multiprocess architecture to prevent client-side bottlenecks during high-concurrency LLM inference testing.

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Source · Sep 18, 2026, 7:04 PM · On Illumora · Sep 18, 2026, 7:07 PM

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Read the source →NVIDIA Developer Blog — Benchmarking LLM Inference at Scale with AIPerf | NVIDIA Technical Blog
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NVIDIA has introduced AIPerf, a successor to GenAI-Perf, engineered to address the challenges of benchmarking large language model (LLM) inference at scale. This new tool incorporates a multiprocess architecture, which aims to prevent the client from becoming a bottleneck when testing LLMs under high concurrency.

AIPerf supports over 15 endpoint types and integrates with public datasets such as ShareGPT, along with trace replay formats from Mooncake, Baseten, and WEKA AgentX, to facilitate realistic workload testing. It also allows engineers to configure various arrival patterns, including constant, Poisson, and gamma distributions, with tunable burstiness to simulate production traffic characteristics.

Key Points

  • AIPerf replaces GenAI-Perf with a multiprocess architecture to avoid client bottlenecks during high-concurrency LLM benchmarking.
  • The tool supports over 15 endpoint types and public datasets like ShareGPT.
  • AIPerf integrates trace replay formats from Mooncake, Baseten, and WEKA AgentX for realistic workload simulation.
  • Configurable arrival patterns, including constant, Poisson, and gamma distributions, allow for shaping load to match production traffic.
  • Core metrics reported include TTFT, ITL, request latency, and output token throughput, with percentile breakdowns.
  • AIPerf can report GPU telemetry (power draw, utilization, memory consumption) when DCGM or pynvml is available.
  • A demonstration uses Qwen3-0.6B served via vLLM to illustrate the measurement loop.

Context

According to the NVIDIA Developer Blog, previous benchmarking methods often suffered from single-process performance limits, Python's GIL capping concurrency, or results measured against self-built references, leading to untrustworthy outcomes and tooling that required frequent rewriting. AIPerf was developed to provide a load client capable of saturating a server without becoming a bottleneck, producing actionable output, and being quick to configure.

Why It Matters

Builders can now obtain more reliable and comprehensive performance metrics for LLM inference, enabling them to optimize deployments and ensure their systems can handle real-world production loads. The detailed metrics and configurable load patterns help identify potential bottlenecks and performance issues before they impact users.

What To Do

  • Explore the AIPerf tutorials to learn advanced benchmarking scenarios.
  • Review the Metrics Reference for full definitions of every reported measurement.
  • Read "How NVIDIA Dynamo 1.0 Powers Multi-Node Inference at Production Scale" for distributed inference benchmarking context.
  • Test AIPerf with a small model like Qwen3-0.6B served through vLLM to establish a measurement loop.