DeFT introduces a new technique for optimizing LLM inference when multiple generation paths share common token prefixes, significantly improving computational efficiency.

• Addresses inefficient memory access patterns in tree-structured LLM applications like few-shot prompting and multi-step reasoning
• Implements Flash Tree-attention to enable proper reuse of cached computations across shared prefixes
• Achieves substantial performance gains by optimizing GPU memory access and utilization
• Provides a practical solution for high-performance, tree-structured inference scenarios

This research matters for engineering teams deploying LLMs at scale, offering concrete methods to reduce computational overhead and improve throughput without sacrificing model quality.

DeFT: Decoding with Flash Tree-attention for Efficient Tree-structured LLM Inference