Speculative Execution

Speculative execution, in the context of large language models (LLMs), aims to accelerate the slow, sequential process of text generation by predicting and pre-computing future tokens. Current research focuses on optimizing various speculative decoding algorithms, including those employing lightweight "draft" models to generate predictions that are subsequently verified by the main LLM, and exploring efficient parallel processing techniques across different hardware architectures. These advancements significantly improve the speed and throughput of LLM inference, impacting both the efficiency of AI services and the feasibility of deploying large models on resource-constrained devices. The ultimate goal is to achieve substantial speedups without compromising the quality of generated text.