Concurrency sweeps help you right-size a generative AI endpoint by finding the instance type and serving configuration that maximizes price-performance while holding latency within acceptable bounds. Without a systematic approach, right-sizing means deploying, load-testing manually, adjusting, and repeating until the numbers look acceptable. Choose five ml.g7e.2xlarge instances when one would suffice, and you burn your budget on idle GPUs. Choose too few, and requests queue, latency spikes, and users experience degraded service.

Concurrency sweeps address this problem. A concurrency sweep is a systematic benchmarking approach that sends controlled, increasing levels of concurrent traffic to your Amazon SageMaker AI endpoint and analyzes its performance. Concurrency sweeps are built into Amazon SageMaker AI Inference Recommendations, so there’s no custom load-testing infrastructure to build or maintain.

In this post, we walk through deploying the NVIDIA Nemotron-3 Nano 30B model, running automated concurrency sweeps, and using the results to make data-driven capacity decisions. By the end, you will know how many concurrent requests your endpoint can handle before latency becomes unacceptable, and how to automate that discovery.

A concurrency sweep sends a controlled number of simultaneous requests to your SageMaker AI endpoint and measures two metrics at each level:

By progressively increasing the concurrency (for example, 64 to 256, and then 1,024 simultaneous requests), you can trace a curve that reveals your endpoint’s saturation point. This is the point where adding more concurrent traffic stops improving throughput and degrades latency.

The following four steps will take you from a fresh deployment to a complete capacity profile.

Source link