Our Mission
Our Mission
RadixArk is an infrastructure-first, deep-tech company building large-scale inference and training systems for the entire AI community. Our mission is simple and ambitious: make frontier-level AI infrastructure open and accessible to everyone.
Today, the most advanced training and inference stacks live inside a few companies. They employ exceptional infrastructure engineers, but their systems primarily serve internal needs. Every new AI lab rebuilds the same schedulers, compilers, serving engines, and training pipelines from scratch. Infrastructure engineers are often treated as a support function—asked to optimize for immediate model metrics rather than long-term system design and first principles. The result is a lot of unintended waste: duplicated effort, underused insights, and slower progress for the broader ecosystem.
RadixArk is an infrastructure-first, deep-tech company building large-scale inference and training systems for the entire AI community. Our mission is simple and ambitious: make frontier-level AI infrastructure open and accessible to everyone.
Today, the most advanced training and inference stacks live inside a few companies. They employ exceptional infrastructure engineers, but their systems primarily serve internal needs. Every new AI lab rebuilds the same schedulers, compilers, serving engines, and training pipelines from scratch. Infrastructure engineers are often treated as a support function—asked to optimize for immediate model metrics rather than long-term system design and first principles. The result is a lot of unintended waste: duplicated effort, underused insights, and slower progress for the broader ecosystem.
01
What we build
What we build
For inference, we build on SGLang — the fastest, most flexible open engine for serving modern models. We will continue investing in SGLang as the performance and reliability foundation for production AI applications.
For RL training, we build on Miles — our open-source framework for large-scale post-training. Miles brings the same rigor to reinforcement learning that modern serving engines brought to inference.
On top of these cores, we ship managed infrastructure and tooling that anyone building AI systems — from individual developers to startups, enterprises, and research labs — can use.
For inference, we build on SGLang — the fastest, most flexible open engine for serving modern models. We will continue investing in SGLang as the performance and reliability foundation for production AI applications.
For RL training, we build on Miles — our open-source framework for large-scale post-training. Miles brings the same rigor to reinforcement learning that modern serving engines brought to inference.
On top of these cores, we ship managed infrastructure and tooling that anyone building AI systems — from individual developers to startups, enterprises, and research labs — can use.
02
How we build
How we build
We treat systems and infrastructure as first-class citizens. That means starting from first principles instead of ad hoc patches, caring about elegance as much as raw throughput, and designing for reliability at frontier scale from day one.
We build in the open whenever we can—contributing code, benchmarks, and architectural insights back to the community rather than hoarding them behind closed APIs. Our business succeeds when the entire ecosystem has access to better infrastructure.
We treat systems and infrastructure as first-class citizens. That means starting from first principles instead of ad hoc patches, caring about elegance as much as raw throughput, and designing for reliability at frontier scale from day one.
We build in the open whenever we can—contributing code, benchmarks, and architectural insights back to the community rather than hoarding them behind closed APIs. Our business succeeds when the entire ecosystem has access to better infrastructure.
03
Where we're going
Where we're going
Our long-term vision is a world where every serious AI builder has access to infrastructure as fast, affordable, and reliable as anything inside the largest companies. The next generation of frontier AI will not be defined by who owns the best private infrastructure, but by who builds the most meaningful applications on top of shared, world-class systems.
We aim to make building, training, and running frontier models at least 10x cheaper and 10x more accessible than they are today. We won’t stop until frontier-level AI infrastructure is a shared foundation that anyone can build on.
Our long-term vision is a world where every serious AI builder has access to infrastructure as fast, affordable, and reliable as anything inside the largest companies. The next generation of frontier AI will not be defined by who owns the best private infrastructure, but by who builds the most meaningful applications on top of shared, world-class systems.
We aim to make building, training, and running frontier models at least 10x cheaper and 10x more accessible than they are today. We won’t stop until frontier-level AI infrastructure is a shared foundation that anyone can build on.
JOIN US!
We're building RadixArk for engineers, researchers, and founders who believe infrastructure should empower the many, not just the few. If you care about first-principles systems design, open infrastructure, and giving real leverage back to the AI community, we'd love to build with you.
We're building RadixArk for engineers, researchers, and founders who believe infrastructure should empower the many, not just the few. If you care about first-principles systems design, open infrastructure, and giving real leverage back to the AI community, we'd love to build with you.