Lossless Acceleration

Lossless acceleration aims to significantly speed up large language model (LLM) inference without sacrificing output quality. Current research focuses on techniques like speculative decoding (using faster, draft models followed by verification), adaptive sparse attention mechanisms, and parallel decoding strategies (generating multiple tokens concurrently). These advancements are crucial for deploying LLMs in resource-constrained environments and improving the efficiency of various applications, including long-context generation and real-time conversational AI.