model compression

Model compression refers to techniques that reduce the size and computational requirements of AI models while preserving performance. On WindowsForum, discussions center on Microsoft's release of open-weight models like gpt-oss-120b and gpt-oss-20b into Azure and Windows AI Foundry, which enable developers to fine-tune and deploy compressed models for custom, privacy-first applications. These models offer unrestricted access to weights, allowing for efficient on-device AI without cloud dependency. Topics include quantization, pruning, and distillation methods that shrink model footprint for faster inference on consumer hardware. The tag covers practical deployment strategies for enterprise and power users seeking to balance model capability with resource constraints.