NVIDIA AIPerf rebuilds LLM inference benchmarking around multiprocess load generation
The successor to GenAI-Perf adds broader endpoints, trace replay, controlled arrival patterns, and percentile-level latency and throughput evidence.
Key takeaways
NVIDIA introduced AIPerf on September 18 as the designated successor to GenAI-Perf. It separates load generation, result processing, and coordination across multiple processes connected through ZMQ so a Python client is less likely to become the bottleneck at production concurrency. AIPerf supports more than fifteen endpoint types, public datasets, and trace replay formats from several serving systems. Engineers can choose constant, Poisson, or gamma arrival patterns, tune burstiness, and ramp concurrency or request rate. Reports include TTFT, inter-token latency, request latency, output-token throughput, percentile distributions, CSV and JSON summaries, logs, and optional GPU telemetry when DCGM or pynvml is available.
Direct answer
NVIDIA introduced AIPerf on September 18 as the designated successor to GenAI-Perf. It separates load generation, result processing, and coordination across multiple processes connected through ZMQ so a Python client is less likely to become the bottleneck at production concurrency. AIPerf supports more than fifteen endpoint types, public datasets, and trace replay formats from several serving systems. Engineers can choose constant, Poisson, or gamma arrival patterns, tune burstiness, and ramp concurrency or request rate. Reports include TTFT, inter-token latency, request latency, output-token throughput, percentile distributions, CSV and JSON summaries, logs, and optional GPU telemetry when DCGM or pynvml is available.
Verified facts
AIPerf is the designated successor to GenAI-Perf and uses multiprocess workers, separate record processors, and ZMQ coordination.
It supports more than fifteen endpoint types plus ShareGPT and trace replay formats from Mooncake, Baseten, WEKA AgentX, and others.
Core outputs include TTFT, ITL, request latency, and output-token throughput with percentile breakdowns, CSV, JSON, and logs.
What changed
- The benchmark client moves from a single process to a multiprocess architecture
- Synthetic lengths and real trace replay use one tool
- Arrival distribution and burstiness become explicit controls
- Latency, throughput, and GPU telemetry can be analyzed together
Impact for AI users
AIPerf does not automatically make a benchmark fair, but it addresses a common error: the load client saturating before the server. Teams must still pin model versions, serving configuration, input and output lengths, streaming mode, and stop conditions, while retaining commands and result files. Good TTFT with poor ITL, or higher throughput with more errors, should never be hidden behind one average.
Operating checklist
- Pin model, serving image, and dataset versions
- Calibrate request arrivals and input-output lengths
- Record percentiles, error rate, and GPU telemetry together
- Save the reproducible command and define regression thresholds
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FAQ
Does AIPerf replace server-side monitoring?
No. It produces external load and results that should be interpreted with server and GPU telemetry.
Why is streaming required?
NVIDIA notes that without streaming there are no first-token or inter-token events to measure.
Summary
AIPerf puts workload shape, client capacity, and result evidence into one reproducible experiment. Reliable conclusions still depend on strict control of variables.
This AI-assisted article is checked by ENHE AI automation for official sources, bilingual fields, media rights, page safety, and historical duplication before publication.
What this means for everyday users
AIPerf does not automatically make a benchmark fair, but it addresses a common error: the load client saturating before the server. Teams must still pin model versions, serving configuration, input and output lengths, streaming mode, and stop conditions, while retaining commands and result files. Good TTFT with poor ITL, or higher throughput with more errors, should never be hidden behind one average.
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