Optimizing Generative AI Inference Latency with Amazon SageMaker Concurrency Sweeps

Over-provisioning inference endpoints leads to unnecessary cloud spend, while under-provisioning triggers latency spikes that degrade user experience. According to the AWS Machine Learning Blog, developers can now mitigate these operational risks by automating load testing through concurrency sweeps directly on Amazon SageMaker AI.

Automating Throughput Benchmarking for LLM Endpoints

The primary challenge in deploying Large Language Models is identifying the specific request-per-second (RPS) threshold where latency exceeds service-level agreements. The CreateAIBenchmarkJob API systematically increases concurrent load on an endpoint, capturing performance metrics that reveal the exact breaking point of your infrastructure. This automated approach replaces manual, error-prone load testing scripts with a managed, native AWS workflow.

Key Takeaways

• Concurrency sweeps identify the saturation point where model response time degrades exponentially.
• Automated benchmarking reduces the time required to calculate required instance counts for peak traffic.
• Data-driven capacity decisions allow for tighter alignment between infrastructure costs and actual inference demand.

Executing Infrastructure Stress Tests via CreateAIBenchmarkJob

To initiate a benchmark, developers define the endpoint configuration and the range of concurrency levels to be tested. The service progressively scales requests to the model, monitoring latency percentiles (P50, P90, P99) at each step. This process yields a detailed performance profile, enabling architectural teams to distinguish between model compute bottlenecks and network-level latency.

Translating Benchmark Results into Fleet Capacity Decisions

Once the saturation point is identified, engineers can calculate the required instance count by dividing peak expected traffic by the throughput-per-instance identified during the sweep. By factoring in the P99 latency results, teams ensure that the provisioned fleet maintains consistent performance even during traffic bursts. This methodology transforms capacity planning from an estimation exercise into a rigorous, evidence-based engineering task.

Integrating Performance Profiles into Production CI/CD

Running these sweeps as part of a model deployment pipeline ensures that every update to the model artifact or endpoint configuration is validated for performance before reaching production. By establishing a baseline for latency and throughput, teams gain the ability to detect regressions early in the lifecycle, preventing performance degradation from reaching end-users.