Inference Optimization
Also called: Inference Performance Optimization
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.
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Related terms
The process of improving an AI model's performance, efficiency, or resource usage through techniques such as fine-tuning, pruning, quantization, or distillation.
QuantizationThe process of reducing the precision of a model's weights and activations to lower memory footprint and speed up inference.
Model ServingThe process of making an AI model available for inference by deploying it behind an interface that accepts requests and returns predictions or generated outputs.
GPU DeploymentThe practice of deploying AI models or applications on graphics processing units (GPUs) to accelerate inference or training by leveraging massively parallel computation for high-performance workloads.
Latency OptimizationThe practice of reducing response latency in AI systems to improve responsiveness, throughput, and user experience.