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NVIDIA Publishes AIPerf Guide for Stress-Testing LLM Endpoints at Scale

September 18, 2026 at 18:38 EDT

Foundation Models
Infra & Chips

NVIDIA has published a new technical guide for NVIDIA AIPerf, a benchmarking tool aimed at answering a production question that simple endpoint checks do not: how an LLM service behaves as traffic rises. The post, titled Benchmarking LLM Inference at Scale with AIPerf, positions the tool around repeatable load tests for Time to First Token, inter-token latency, overall latency and throughput.

Sep. 18, 2026 · NVIDIA

AIPerf stress-tests LLM endpoints

The tool measures Time to First Token, inter-token latency, request latency and throughput under repeatable traffic patterns, showing when a working endpoint becomes a capacity problem.

100

Concurrent users in the documented run

1,000

Requests sent during the test

347.15 ms

Average Time to First Token

22,521.42

Output tokens per second

Latency stretches under over-saturation

~250 ms

Concurrency 10

~1,129 ms

Concurrency 500

Each block represents roughly 125 ms of average TTFT; the documented sweep shows how queueing can overwhelm raw capacity.

Generate load

Measure tokens

Find bottlenecks

Choose target

Operational upside

Repeatable tests expose latency tails, throughput ceilings and service-level trade-offs before traffic reaches production scale.

Important caveat

Demo results are not universal benchmarks; reasoning-token behavior and unsupported migration features can affect comparisons.

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