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structured decoding, a guide for the impatient

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vLLM is the high-throughput and efficient inference engine for running large-language models (LLM). 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 a tentative roadmap for vLLM’s v1 improvement for structured decoding. 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 system. For more information about vLLM, please check out our documentation. If you have read about the history of the field before, feel free to skip this part to reason for structured decoding The inception of AI might well be traced back to the origin of logics, where men put emphasis on reducing reasoning to some specific sets of calculations (a reductionist ap

tldr : 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 and schedule-level mask broadcasting for mixed-requests batch support vLLM is the high-throughput and efficient inference engine for running large-language models ( LLM ). In this post, we will explore the annotated history of language models, describe the current state of structured decoding in

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