엔비디아 AI

Separate training from inference optimization and serving. A model may be trained in a framework, converted or optimized for execution, and then exposed through a service. Each stage has compatibility requirements and can change the behavior or resource use of the final system. Check the specific hardware, numerical formats, software versions, and supported operations. An optimization available on one device or runtime may not be available on another. Record the configuration used for any benchmark. Measure memory and data movement alongside arithmetic throughput. Long inputs, concurrent requests, and cached model state can change the bottleneck. A larger accelerator does not automatically improve a workload limited by preprocessing, network transfer, or a downstream service. Compare the deployed output with the original model after optimization. Lower precision and alternative execution paths can affect accuracy. Evaluate latency, throughput, power, and cost using the intended application conditions, and consult current documentation for compatibility and maintenance requirements.