Radix Ark offers an open‑source, infrastructure‑first platform for large‑scale AI training and inference. Its Miles framework provides a high‑throughput, reliable foundation for reinforcement‑learning and post‑training workloads, while managed services, tooling, and APIs let developers, research labs, and enterprises run frontier AI models without building their own low‑level stack.
Funding
Funding not disclosed
Founders
Product
Problem
Developing and deploying large‑scale AI models requires complex training and inference infrastructure that is typically built in‑house by a few well‑funded organizations. Smaller labs, startups, and individual developers must recreate schedulers, compilers, and serving engines from scratch, leading to duplicated effort, underutilized expertise, and slower progress across the AI ecosystem.
Solution
RadixArk provides an open, infrastructure‑first platform that delivers high‑throughput training and inference systems for the broader AI community. At its core is the open‑source Miles framework, which offers a rigorous, scalable foundation for reinforcement‑learning and post‑training workloads. Building on this core, RadixArk offers managed services, tooling, and APIs that enable developers, research labs, and enterprises to run frontier‑level AI workloads without constructing their own low‑level stack. The company emphasizes first‑principles system design, reliability at scale, and open contribution of code, benchmarks, and architectural insights, allowing users to focus on model development rather than infrastructure engineering.
Target Audience
Primary customers are AI engineers, researchers, and product teams at startups, enterprises, and independent labs that need robust, scalable training and inference infrastructure without building it from scratch.
Features
- Open‑source Miles framework for large‑scale reinforcement learning and post‑training pipelines
- Managed training and inference infrastructure with high‑throughput scheduling and compilation
- Scalable serving engine designed for reliability at frontier AI workloads
- Comprehensive tooling and APIs for seamless integration into existing AI development workflows
- Community‑driven development model with public code releases, benchmarks, and architectural documentation
- Support for a range of users from individual developers to enterprise research labs