Request Batching

The process of running model inference on multiple inputs together to improve throughput, resource utilization, and overall serving efficiency.

The process of improving the speed, latency, throughput, memory usage, or cost-efficiency of AI model inference through techniques such as quantization, pruning, batching, caching, hardware acceleration, and optimized model serving.

The practice of reducing response latency in AI systems to improve responsiveness, throughput, and user experience.

The amount of data, tokens, or requests processed by a system within a specified period.

The automatic adjustment of computing resources in response to changing workloads to optimize performance, availability, and cost efficiency.