✳flâneur — a map of the web's best reading

Structured Decoding in vLLM: A Gentle Introduction

bentoml.com · 2,617 words · saved by 1 readers

vLLM is the high-throughput and efficient inference engine for running large-language models (LLMs). In this post, we will explore the annotated history of language models, describe the current state of structured decoding in vLLM, as well as the recent integration with XGrammar, and share our tentative roadmap for future improvements. We would also invite users to tackle this blog post from a philosophical perspective, and in the process trying to posit that structured decoding represents a fundamental shift in how we think about LLM outputs. It also plays an important role in building complex agentic systems. In 1950, Alan Turing proposed that a high-speed digital computer, programmed with rules, could exhibit emergent behaviour of intelligence (Turing, 1950). This led to two main approaches in AI development: In summary: LLMs excel at the following heuristic: given a blob of text, the model will generate a contiguous piece of text that it predicts as the most probable tokens. For ex

Structured Decoding in vLLM: A Gentle Introduction Engineering Engineering Structured Decoding in vLLM: A Gentle Introduction Understand structure decoding and vLLM and how recent XGrammar integration can contribute to 5x improvement in TPOT. Authors Aaron Pham Last Updated January 15, 2025 Share TL;DR Structured decoding allows precise control over LLM output formats vLLM now supports both outlines and XGrammar backends for structured decoding Recent XGrammar integration brings up to 5x improvement in time per output token (TPOT) under load Upcoming v1 release focuses on enhanced performance

Explore this link on the map →

related reading