HeadInfer introduces a novel approach to overcome memory limitations when running large language models, by intelligently managing how attention components are stored and processed.

• Offloads key-value (KV) cache to CPU RAM using a fine-grained, head-wise strategy
• Maintains only a subset of attention heads on GPU at any time
• Significantly reduces GPU memory requirements during inference
• Enables processing of longer context without performance degradation

This engineering advancement matters because it allows organizations to run larger models on existing hardware, reducing infrastructure costs while maintaining performance for long-context applications.

HeadInfer: Memory-Efficient LLM Inference by Head-wise Offloading