Faster, cheaper, just as smart: Improving the economics of LLM inference with speculative decoding

Modern large language models (LLMs) are defined by their scale. GPT-3 introduced 175 billion parameters in 2020, and today, production-grade models routinely operate in the hundreds of billions, with some architectures exceeding one trillion. Each parameter represents a learned weight, collectively encoding the language, reasoning, and knowledge that make these systems capable.This scale is not incidental.