Model Compression
Techniques to reduce AI model size and computational requirements while preserving performance for deployment efficiency.
Definition
Model compression encompasses various techniques including pruning, quantization, and knowledge distillation to reduce model size and computational demands. These methods enable deployment of powerful AI capabilities on resource-constrained devices and reduce infrastructure costs.
Compression techniques balance performance preservation with size reduction, ensuring models remain effective while becoming more practical for real-world deployment scenarios.
Why It Matters
Compressed models enable edge computing deployments, reduce cloud infrastructure costs, and improve response times for user-facing applications. This democratizes access to advanced AI capabilities across different hardware configurations.
Businesses can deploy sophisticated AI solutions on mobile devices, IoT hardware, and cost-effective cloud instances while maintaining competitive performance levels and user experiences.
Examples in Practice
Apple compresses Siri's language models to run efficiently on iPhones while maintaining conversational quality and response speed.
Google compresses computer vision models for real-time photo processing in Android cameras, enabling advanced features without cloud connectivity.
Microsoft compresses Office AI features to run locally, providing document assistance and writing suggestions without sending data to external servers.