I-AI Inference Optimization

Techniques include batching, caching, lower precision, model selection, and efficient execution. Choose them from a measured bottleneck rather than assuming every optimization helps every workload.

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• Benchmark realistic workloads.
• Retest quality after numerical changes.
• Optimize the dominant stage of the complete request.

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.

1. In a constructed request, model execution takes 400 ms and all other work takes 600 ms.
2. Making the model twice as fast reduces total time from 1,000 ms to 800 ms: a 20% end-to-end reduction.
3. Measure the other stages before assuming another model optimization is the highest-value change.

The invented timing example illustrates why a component speedup is not the same as a system speedup.