AI Inference Optimization

Measure end-to-end latency, throughput, memory, and task quality on realistic inputs. Include cold starts, concurrency, long requests, and cancellation. A benchmark using one short warmed-up input may not represent a user-facing service. Batching can improve hardware use by processing requests together, but waiting to form a batch can increase individual latency. Caching helps repeated work only when the cache key captures the relevant model, input, permissions, and version. Incorrect caching can return stale or unauthorized results. Lower precision or quantization can reduce memory and computation, but the quality impact depends on the model, hardware, method, and task. Compare against the original configuration using the same evaluation examples, including rare and numerically sensitive cases. Optimize the complete request path. Retrieval, tokenization, network transfer, and output handling may dominate the model execution time. Change one meaningful factor at a time and record both the improvement and any regression. The objective is a better completed task, not a more flattering isolated throughput number.