How speculative decoding delivers faster LLM inference

This blog post explains the concept of speculative decoding, a technique that enhances the performance of large language models (LLMs) by combining the strengths of a fast speculator model and a more comprehensive verifier model. By allowing the speculator model to generate multiple tokens in parallel, it reduces the bottleneck in token generation seen in traditional methods, achieving speed improvements without sacrificing output quality. It's especially beneficial for predictable workloads like code generation and structured outputs, resulting in significant speed and cost efficiencies in production environments.