AttentionPredictor introduces a novel approach to KV cache compression that considers temporal patterns in attention scores for more efficient LLM inference, particularly during long-context generation.

• Improves efficiency by predicting future attention patterns rather than relying solely on past attention scores
• Achieves up to 4× compression rate with minimal performance degradation
• Uses a lightweight MLP-based predictor that analyzes historical attention patterns
• Enables faster inference while maintaining output quality for long-context applications

This engineering advancement addresses a critical bottleneck in LLM deployment by reducing memory requirements and computational costs without sacrificing performance quality—essential for scaling AI systems in resource-constrained environments.

AttentionPredictor: Temporal Pattern Matters for Efficient LLM Inference