Performance Tuning

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 process of reducing infrastructure, model, and operational costs while maintaining or improving application performance, reliability, and quality.

The process of improving an AI model's performance, efficiency, or resource usage through techniques such as fine-tuning, pruning, quantization, or distillation.

An optimization technique that combines multiple incoming requests into a single batch to improve throughput and resource utilization.

A performance optimization technique that stores previously generated responses to immediately serve identical future requests.