Model Compression reduces the size and computational requirements of AI models while preserving most of their performance. Common techniques include pruning, quantization, and knowledge distillation.
Voice AI providers compress models to deploy speech recognition and language models on mobile devices, edge hardware, and latency-sensitive production environments with lower infrastructure costs.