Answer extracted from the AI + a16z podcast — listen to the full episode below.
How does vLLM function within the modern AI infrastructure stack for enterprises using open-source models?
vLLM is an inference engine that transforms available GPUs into running endpoints for AI intelligence, functioning as critical infrastructure alongside databases and operating systems in modern AI stacks. It supports more than 1,000 model architectures and is currently running on half a million GPUs globally, serving as a performance benchmark for hardware vendors like NVIDIA, AMD, Google, Amazon, and Intel.
GPU acceleration as the foundation
vLLM's core role is managing the computational intensity of LLM serving, which requires rapid execution on accelerators like GPUs or TPUs — a fundamentally different challenge than traditional machine learning inference. Rather than treating inference as a secondary concern, Simon Mo and his team recognized that serving open-source models demands an entirely new infrastructure layer, one that vendors now rely on to validate their hardware performance.
As the episode explains, major chip manufacturers have adopted vLLM as a benchmark tool: when they release new hardware, they test it against vLLM to ensure their latest chips can deliver the performance enterprises demand.
Day Zero support and vendor partnerships
vLLM implements new model architectures immediately upon their public release in a practice called Day Zero model release support. This means enterprises deploying cutting-edge open-source models like Llama, Mistral, or the latest variants can run them on vLLM without waiting for proprietary optimizations or vendor-specific implementations.
The engine's breadth—supporting over 1,000 model architectures—reflects deep collaboration with the open-source community and hardware partners. This positions vLLM not as a niche tool but as the critical infrastructure layer enabling enterprises to run production workloads on open models without proprietary lock-in.
"Every provider can offer potentially even 10 different levels of speed going from the slowest mode which can be a lot cheaper to 400 tokens per second almost up to 500 in many cases."
Simon Mo — Co-founder and CEO of Infraact, lead maintainer of vLLM. Mo founded the vLLM project in 2022, originally aimed at accelerating a slow open-source demo but which evolved into discovering and solving a pile of unsolved problems in LLM serving infrastructure. His work has made vLLM the foundation for inference optimization across the industry, with the engine now running on half a million GPUs at any moment.
This flexibility—enabling different speed tiers from cost-optimized modes to ultra-fast inference—shows how vLLM adapts to real enterprise needs. Organizations can tune throughput and cost based on their use case, something proprietary stacks rarely offer with such granularity.
To understand the broader context of why open-source models require different serving paradigms than proprietary ones, listen to the full discussion between Simon Mo and the a16z team, where they explore the infrastructure economics driving enterprise adoption of open-weight models.
See also
What makes serving a large language model fundamentally different from traditional machine learning workloads?
Serving a large language model requires running it on accelerators like GPUs or TPUs with computationally intensive processes that need quick and efficient handling, fundamentally different from traditional ML workloads.