Amazon SageMaker AI Inference Recommendations gets automated concurrency sweeps for sizing AI endpoints
Amazon SageMaker AI Inference Recommendations now offers automated concurrency sweeps – built-in load testing that finds the saturation point of an endpoint without the need for your own load-testing infrastructure. Demonstrated using the NVIDIA Nemotron-3 Nano 30B model.
AWS has expanded the Amazon SageMaker AI Inference Recommendations tool with a concurrency sweep feature – systematic load testing that sends a progressively increasing number of concurrent requests to an endpoint and measures both latency and throughput. According to the source, the feature is built directly into SageMaker AI, so there is no need to build or maintain your own load-testing infrastructure. The goal is to find the so-called saturation point – the point beyond which additional load no longer increases throughput, but latency rises sharply.
The process is demonstrated by deploying the NVIDIA Nemotron-3 Nano 30B model (a Mixture-of-Experts architecture with 3B active parameters) on an ml.g7e.2xlarge instance with an NVIDIA Blackwell GPU, using a vLLM Deep Learning Container configured through SM_VLLM_* variables. Because of the hybrid Mamba-Transformer architecture in Nemotron-3 Nano, eager execution mode is required (the SM_VLLM_ENFORCE_EAGER flag); GPU memory utilization was set to 0.85 to leave room for KV cache growth under higher load, and prefix caching was enabled for scenarios with repeated system prompts.
The load profile in the example assumed an average of 1024 input and 256 output tokens and response streaming to measure the time to first token metric. The benchmark job (via the CreateAIBenchmarkJob API, using the AIPerf engine) works through the concurrency levels sequentially so that each measurement is isolated. In the described test scenario, the saturation point occurred at 256 concurrent requests – below this threshold, operation is within the safe range; above it, according to the source, the endpoint becomes overloaded and users have to wait.
The full text of the source article is not available, so the summary does not cover the subsequent steps (automatically finding the optimal operating level across model versions and instance types). Details can be found in the source article.
Why it matters
According to the source, manual load testing when choosing an instance and configuration for a generative AI endpoint is a time-consuming process of trial and error, where a poor capacity estimate leads either to waste from unused GPUs or to degraded latency for users. A built-in, automated tool simplifies this process and makes it accessible even to teams that would not build their own load-testing infrastructure.
Two audiences, two different impacts
What this means
For individuals
For ML and platform engineers deploying models on Amazon SageMaker AI, repeated manual load testing and instance tuning are replaced by an automated sweep directly within the tool.
For a business
According to the source, this allows companies running generative AI inference on AWS to size instances more accurately, which can reduce spending on unused GPU capacity or, conversely, prevent latency degradation caused by undersizing an endpoint.
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