Model Context Protocol (MCP)
AI Infrastructure
Techniques used to reduce the size and computational requirements of neural networks while maintaining performance. This enables deployment on resource-constrained devices.
Detailed Explanation
Model compression involves techniques to reduce neural network size and computational demands without significantly affecting accuracy. Methods include pruning, quantization, and knowledge distillation. These techniques optimize models for deployment on devices with limited resources, such as smartphones and IoT devices, enabling efficient inference, faster processing, and lower energy consumption while maintaining the model's core capabilities.
Use Cases
- •Deploying AI models on mobile devices to ensure efficient, low-latency inference without heavy resource use.
Related Terms
Other terms in the AI Infrastructure category