💬 TL;DR Lilac is an open-source tool that ensures your data scientists always have enough GPUs for their work. We seamlessly connect compute from any source, on-prem or cloud. Check out our GitHub!
❗The Problem: The GPU Scramble is Slowing Down AI Innovation
Ask any data scientist and they will likely tell you their biggest frustration is finding GPUs. One team we talked to is spending $7 million dollars a year with a service provider to guarantee GPUs, yet they were still running out. This forces data science and infrastructure teams into a difficult position:
🏆 The Solution: Unify Your Compute, Unleash Your Data Scientists
Lilac is an open-source platform that transforms your scattered, multi-cloud, and on-premise GPU resources into a single, unified compute fabric.
Instead of hunting for servers, your data scientists simply submit their training jobs to Lilac. Our intelligent scheduler handles the rest—finding the first available GPU that meets the job's requirements, allocating it, and running the job.
The Result:
Lilac bridges the gap between R&D and infrastructure, turning GPU scarcity into a streamlined, efficient workflow.
Here’s an introduction and example of how it works:
https://www.youtube.com/watch?v=n8Ye-8YS0I4&ab_channel=Lilac<div class="embed-container youtube relative pb-[56.25%] h-0">
🥇 About Us
We're a team of two brothers, @Ryan Ewing and @Lucas Ewing, who have spent our careers building the infrastructure that powers the modern web. With experience scaling enterprise infrastructure at AWS and building end-to-end data platforms at Webflow, we've seen firsthand how critical, and how broken, the tooling for AI development is. We believe the rapid acceleration of AI requires a fundamental rethinking of the infrastructure stack, and we built Lilac to be the foundation of that new ecosystem.
⭐ Our Ask
We are thrilled to announce that Lilac is now open-source with the release of v0.1.0!