From Rigid to Dynamic: Entropy-Guided Adaptive Inference for Long-Context LLMs

Researchers introduce EntropyInfer, a training-free framework that optimizes long-context LLM inference by dynamically allocating computational resources based on attention entropy patterns. The method achieves up to 2.39× speedup on models like Llama and Qwen beyond 100k tokens while maintaining output quality, addressing limitations in existing sparse attention and KV cache compression techniques.

EntropyInfer addresses a critical bottleneck in deploying large language models at scale: the computational cost of processing extended context windows. As organizations push LLMs toward longer sequences—essential for document analysis, code understanding, and multi-turn conversations—inference efficiency becomes paramount. The framework's key insight recognizes that attention heads behave heterogeneously; some remain inactive regardless of input while others respond dynamically to context variations. By measuring entropy as a proxy for attention behavior, EntropyInfer enables granular resource allocation without requiring model retraining.

This builds on years of sparse attention research attempting to reduce the quadratic complexity of standard attention mechanisms. Previous approaches like SnapKV and AdaKV applied uniform compression strategies, treating all attention heads identically. EntropyInfer's context-aware approach represents a meaningful evolution, leveraging the observation that optimal sparsity patterns emerge during inference rather than being predetermined. The introduction of latent KV cache compression using generated tokens rather than prefill tokens alone creates an additional efficiency gain by identifying which historical context remains relevant as the model generates new output.

For practitioners, the implications are significant. Achieving 2.39× speedup translates directly to reduced latency and lower infrastructure costs for production systems. The training-free nature eliminates integration friction—practitioners can apply EntropyInfer to existing models without retraining. The approach demonstrates competitive or superior performance across multiple model families, suggesting broad applicability. As context lengths expand beyond 100k tokens and models become larger, efficient inference mechanisms like this become strategic differentiators for organizations building AI applications.

• →EntropyInfer uses attention entropy to dynamically allocate compute resources per attention head and segment without requiring model retraining.
• →The framework achieves up to 2.39× end-to-end speedup for long-context inference beyond 100k tokens across Llama, Qwen, and openPangu models.
• →Distinguishing between rigid heads and dynamic heads enables more efficient resource allocation than uniform compression strategies.
• →Latent KV cache compression using generated tokens improves efficiency beyond previous prefill-only approaches.
• →Training-free implementation allows immediate deployment on existing models without retraining overhead.