D2O introduces a novel KV cache compression method that dynamically optimizes memory usage while preserving generation quality for large language models processing long context.

• Reduces memory demands by 67-83% while maintaining output quality
• Employs discriminative operations to intelligently determine which context to retain
• Achieves up to 2× speedup in inference without compromising effectiveness
• Compatible with existing LLM architectures without retraining

This engineering breakthrough enables processing of significantly longer sequences with existing hardware resources, making advanced LLM applications more practical and cost-effective for businesses.

D2O: Dynamic Discriminative Operations for Efficient Long-Context Inference of Large Language Models