<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>VLLM Alternative on SoloSoft</title><link>https://www.solosoft.dev/tags/vllm-alternative/</link><description>Recent content in VLLM Alternative on SoloSoft</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.solosoft.dev/tags/vllm-alternative/index.xml" rel="self" type="application/rss+xml"/><item><title>SGLang: Efficient LLM Inference with Structured Generation</title><link>https://www.solosoft.dev/post/sglang-inference-2026/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://www.solosoft.dev/post/sglang-inference-2026/</guid><description>&lt;p&gt;The open-source LLM ecosystem has solved many problems — model quality, fine-tuning, deployment — but one challenge persists: getting models to produce reliable, structured output. A model asked to output JSON might add explanatory text, use inconsistent key names, or fail to close brackets. For production systems that feed LLM output into downstream APIs, databases, or parsers, this unpredictability is a blocker.&lt;/p&gt;
&lt;p&gt;SGLang approaches this problem from the inference engine level rather than the prompting layer. It is a high-performance LLM inference framework that builds structured generation into the core inference pipeline. Instead of asking the model nicely to output JSON and hoping for the best, SGLang constrains the token generation process so that every token is guaranteed to conform to a specified grammar, schema, or pattern.&lt;/p&gt;</description></item></channel></rss>