Build an AI General Inference Layer! How does the vLLM open-source project become an ambitious plan to become a global inference engine?

As AI models evolve rapidly, efficiently performing inference on these large-scale models has become a key challenge that the industry cannot ignore. The open-source project vLLM from UC Berkeley not only directly tackles this technical challenge but also gradually builds its own community and ecosystem, even inspiring a startup called Inferact focused on inference infrastructure. This article will take you deep into vLLM’s origins, technological breakthroughs, open-source community development, and how Inferact aims to create a “universal AI inference engine.”

From academic experiments to GitHub star project: the birth of vLLM

vLLM originated from a Ph.D. research project at UC Berkeley aimed at solving the low inference efficiency of large language models (LLMs). At that time, Meta open-sourced the OPT model, and one of vLLM’s early contributors, Woosuk Kwon, attempted to optimize the demo service for that model, discovering an unresolved challenge in inference systems behind it. “We thought it would take just a few weeks, but it opened up a whole new path of research and development,” Kwon recalls.

Bottom-up challenge: why is LLM inference different from traditional ML?

vLLM targets autoregressive (auto-regressive) language models, whose inference process is dynamic, asynchronous, and cannot be batched, making it very different from traditional image or speech models. The input length for these models can range from a single sentence to hundreds of pages of documents, requiring precise GPU memory management. The computation steps (token-level scheduling) and memory management (KV cache handling) also become particularly complex.

One significant technical breakthrough of vLLM is “Page Attention,” a design that helps the system manage memory more efficiently and handle diverse requests and long output sequences.

More than just coding: from campus to open-source community milestones

In 2023, the vLLM team held its first open-source meetup in Silicon Valley. They initially expected only a dozen participants, but the registration far exceeded expectations, filling the venue and marking a turning point in community development.

Since then, the vLLM community has grown rapidly, now with over 50 regular contributors and more than 2,000 GitHub contributors. It is one of the fastest-growing open-source projects today, receiving support from Meta, Red Hat, NVIDIA, AMD, AWS, Google, and others.

Multiple forces competing: building an “AI Operating System”

One of vLLM’s keys to success is that it provides a common platform for model developers, chip manufacturers, and application developers. They do not need to interface with each other directly; connecting to vLLM alone maximizes compatibility between models and hardware.

This also means vLLM is attempting to create an “AI operating system”: enabling all models and hardware to run on a single universal inference engine.

Inference getting harder? The triple pressure of scale, hardware, and agent intelligence

Today’s inference challenges are continuously escalating, including:

• Explosive model scale: from hundreds of millions of parameters initially to trillion-parameter models like Kim K2, requiring exponentially more computational resources.
• Diversity of models and hardware: While Transformer architecture remains consistent, internal details are increasingly divergent, with variants like sparse attention and linear attention emerging.
• Rise of agent systems: Models are no longer just answering one question but participating in continuous conversations, calling external tools, executing Python scripts, etc. Inference layers need to maintain long-term state and handle asynchronous inputs, raising technical thresholds further.

Real-world deployment: cases of vLLM in large-scale applications

vLLM is not just an academic toy; it has been deployed on major platforms like Amazon, LinkedIn, and Character AI. For example, Amazon’s intelligent assistant “Rufus” is powered by vLLM, serving as the inference engine behind shopping search.

Some engineers have even deployed vLLM on hundreds of GPUs during development of features still in progress, demonstrating high community trust.

The company behind vLLM: Inferact’s role and vision

To promote further development of vLLM, the core developers founded Inferact, which has received multiple investments. Unlike typical commercial companies, Inferact prioritizes open source. Co-founder Simon Mo states, “Our company exists to make vLLM the global standard inference engine.” Inferact’s business model revolves around maintaining and expanding the vLLM ecosystem, providing enterprise deployment and support, creating a dual track of business and open source.

Inferact is actively recruiting engineers with ML infrastructure experience, especially those skilled in large model inference, distributed systems, and hardware acceleration. For developers seeking technical challenges and deep system optimization, this is an opportunity to participate in the next-generation AI infrastructure.

The team aims to create an “abstraction layer” similar to an OS or database, allowing AI models to run seamlessly across diverse hardware and application scenarios.

This article: Building a universal AI inference layer! How the open-source project vLLM aims to become a global inference engine? Originally published on Chain News ABMedia.