Inferential Statistics
AI Infrastructure
Techniques (quantization, pruning, compilation) to make trained AI models run faster and more efficiently.
Detailed Explanation
Inference Optimization involves techniques such as quantization, pruning, and compilation to enhance the speed and efficiency of trained AI models during deployment. These methods reduce model size, lower computational requirements, and improve latency, enabling faster responses and lower resource consumption in real-world applications without significantly sacrificing accuracy. It is essential for deploying AI models effectively at scale.
Use Cases
- •Implementing inference optimization to deploy real-time chatbots with minimal latency and resource usage on edge devices.
Related Terms
Other terms in the AI Infrastructure category