RadixArk Raises $100M in Seed Funding Led by Accel

The company is valued at 400 million. The company will use the funding to grow SGLang, add support for new AI models and advanced hardware, and build large-scale systems for training and running next-generation AI applications.

In 2023, Sheng, and others created SGLang an open source system for running AI models at scale, which quickly became widely adopted and is now supported by a global community across companies, universities, and research groups, running on hundreds of thousands of GPUs, and generating trillions of tokens daily for organizations like Google, Microsoft, NVIDIA, Oracle, AMD, LinkedIn and others.

RadixArk is building a full AI platform that goes beyond basic model hosting, supporting the entire process from training custom models and fine-tuning open models to running reinforcement learning and deploying them at scale, so customers can manage everything in one place while keeping full control of their models and using high-quality infrastructure.

RadixArk builds its platform on proven open technologies across the AI stack, using SGLang for fast, flexible model deployment and Miles for large-scale training. SGLang was originally developed at LMSys, a nonprofit founded by researchers from leading universities such as Stanford, Carnegie Mellon, and UC Berkeley.

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“SGLang is the absolute best inference framework for large language models,” said Igor Babuschkin, Co-Founder of xAI and RadixArk angel investor. “It was a crucial part of the infrastructure at xAI, because it enabled folks to run large models faster and more efficiently than many alternatives. I’m excited to see Ying and Banghua expand that vision with RadixArk.”

“Durable technology shifts are built on infrastructure that empowers entire ecosystems, not just individual companies,” said Lip-Bu Tan, CEO of Intel and RadixArk angel investor. “RadixArk has a compelling mission to build the next-generation AI infrastructure stack, and SGLang is already emerging as a dominant inference engine for large models. I’m glad to support the company as an early investor.”

“Some of the most important software of the last decade started as open source projects run by small groups of researchers who refused to compromise on quality. SGLang sits in that lineage, born at LMSys, maintained by thousands of contributors, now the de facto standard for modern inference. RadixArk carries that same spirit into a company, and we're honored to help Ying, and Banghua scale it,” said Arpan Shah, general partner at Spark Capital. “Frontier AI is at risk of becoming the private infrastructure of a handful of companies. RadixArk is the counterweight: a belief that the next-generation of AI products will be built on open, shared systems that any team can run, tune and own.”

“RadixArk is building the open foundation for the next era of AI—where companies don’t just consume models, they train and manage them as a core part of product development,” said Ivan Zhou, partner at Accel. “By democratizing training and inference infrastructure, RadixArk enables any engineer to experiment and innovate at the frontier, fully owning how AI powers their products.”

“Our mission is simple yet ambitious, make frontier level AI infrastructure open and accessible to everyone,” said Ying Sheng, co founder and CEO of RadixArk. “We believe the next generation of AI won’t be defined by who owns the biggest private infrastructure, but by who builds the most meaningful applications on top of shared, world class systems. We aim to make these systems orders of magnitude cheaper, and more accessible so that everyone can build on them.”

About RadixArk

Founded by Ying Sheng and Banghua Zhu, RadixArk is an AI infrastructure company building open, scalable systems for training, deploying and running advanced models, developed by the team behind SGLang. It offers a full platform that combines inference, training, and post training using tools like SGLang, and Miles, along with managed infrastructure to help developers, startups, enterprises, and research labs build, and run AI systems faster and more efficiently.

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