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“vLLM is sophisticated software designed to feel easy and intuitive. RunLLM makes that vision real. Instead of sifting through lengthy documentation or old issues, users now get exactly what they need instantly. That’s transformative.” — Simon Mo, vLLM Core Maintainer
vLLM is an open-source library that provides high-performance inference and serving for large language models (LLMs). Originating as a UC Berkeley research project in 2023, vLLM quickly achieved broad adoption, earning over 45,000 GitHub stars and attracting thousands of contributors.
Model creators such as Meta and Mistral actively use vLLM to support their latest LLMs. Hardware providers including NVIDIA, AMD, Google, AWS, and Intel regularly contribute, optimizing vLLM for their chips. Enterprise technology companies like Snowflake, Red Hat, and Anyscale directly integrate vLLM into their hosted services.
In 2024, vLLM was actively supported by more than 15 full-time contributors from leading organizations like UC Berkeley, Neural Magic, Anyscale, Roblox, IBM, AMD, Intel, and NVIDIA, as well as independent developers around the world. Over 20 prominent organizations participated closely through regular office hours, creating a thriving community of model creators, hardware vendors, and optimization specialists. Yet the core maintenance team remained lean and entirely volunteer-based.
Initially, the team answered technical support questions through GitHub Issues and email. As the popularity of vLLM surged, questions flooded in faster than volunteers could respond.
“We answered questions whenever we could, but it wasn’t sustainable. Most required deep technical knowledge of model architectures and hardware details. Volunteers couldn’t keep up.” — Simon Mo, vLLM Core Maintainer
This influx slowed development and left users frustrated. vLLM urgently needed a scalable way to provide expert-level support for its rapidly growing open-source community.
Simon and the other maintainers evaluated typical chatbot solutions but dismissed them quickly.
“We categorically rejected most AI assistants. They were inaccurate and generic. Our users needed precise answers reflecting our exact technology and terminology.” — Simon Mo, vLLM Core Maintainer
Then Simon tested RunLLM himself. After rigorous evaluation, RunLLM stood apart in delivering high-quality, detailed responses tailored specifically to vLLM’s complex ecosystem.
“RunLLM was the first solution that actually worked. We tested it rigorously. It delivered precise answers tailored to our software and terminology. We trust it implicitly.” — Simon Mo, vLLM Core Maintainer
Deployment was straightforward. RunLLM integrated seamlessly with vLLM’s existing support channels, including GitHub Issues, Slack, documentation site, and Discourse forums. It continually learned from maintainers’ feedback and user interactions.
RunLLM quickly became the default first line of response. And users now are encouraged to consult RunLLM before escalating issues.
“RunLLM immediately became our front line, and now handles 99% of all community questions. Users ask RunLLM first. They can escalate, but most don’t — they trust its answers.” — Simon Mo, vLLM Core Maintainer
Today, maintainers additionally use RunLLM internally as a co-pilot for troubleshooting, often redirecting community members to RunLLM-generated answers.
RunLLM transformed the support experience for vLLM:
Most significantly, RunLLM strengthened vLLM’s core mission: to simplify a deeply complex technology.
“vLLM is sophisticated software designed to feel easy and intuitive. RunLLM makes that vision real. Instead of sifting through lengthy documentation or old issues, users now get exactly what they need instantly. That’s transformative.” — Simon Mo, vLLM Core Maintainer
Facing overwhelming technical support demand in your community or user base? Effortlessly scale expert-level support, accelerate adoption, and deliver trustworthy answers with RunLLM.
Visit runllm.com to learn more.
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