Lilac recently launched!

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^{"Lilac! The open-source tool that ensures your data scientists always have GPUs."}

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^{💬 TL;DR Lilac is an open-source tool that ensures your data scientists always have enough GPUs for their work. They seamlessly connect compute from any source, on-prem or cloud. Check out their GitHub!}

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Founded by Lucas Ewing & Ryan Ewing‍

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🥇 About The Founders

The Lilac founders are a team of two brothers, @Lucas Ewing and @Ryan Ewing, who have spent their 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, they've seen firsthand how critical, and how broken, the tooling for AI development is. At Lilac, they believe the rapid acceleration of AI requires a fundamental rethinking of the infrastructure stack, and the founders built Lilac to be the foundation of that new ecosystem.

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❗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 the founders 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:

• Wasted Time: Data scientists waste hours hunting for available GPUs across different clouds and on-premise servers, distracting them from building models.
• Spiraling Costs & Security Risks: Infrastructure teams lose visibility as desperate data scientists use personal keys on unmanaged instances, leading to security gaps and unpredictable cloud bills.
• Slowed Innovation: The result is a direct trade-off between innovation speed and engineering best practices. Companies either accept a slow model development lifecycle or pay millions for dedicated GPU contracts in a single cloud, losing flexibility.

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🏆 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. Their intelligent scheduler handles the rest—finding the first available GPU that meets the job's requirements, allocating it, and running the job.

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The Result:

• For Data Scientists: Maximum productivity. They can focus on building models, not managing infrastructure.
• For Infrastructure Teams: Centralized control. Gain full visibility, enforce security policies, and manage costs across your entire fleet of compute resources.

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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

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The founders are thrilled to announce that Lilac is now open-source with the release of v0.1.0!

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Learn More

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^{🌐 Visit www.getlilac.com to learn more.}

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^{👉 Check out their GitHub here.}

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^{⭐ Try it out: Star the repo, try the quick start, and give the founders your feedback. They welcome all contributions, from bug reports to feature requests.}

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^{🤝 Get in touch: If the problem Lilac is solving resonates with your company, the founders would love to connect. Reach out via email here, schedule a 15-minute chat here, or checkout the website.}

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^{👣 Follow Lilac on LinkedIn & X.}

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