<p><strong>PALO ALTO</strong> &#8212; <a href="https://cts.businesswire.com/ct/CT?id=smartlink&;url=https%3A%2F%2Fwww.radixark.ai%2F&;esheet=54528914&;newsitemid=20260505077157&;lan=en-US&;anchor=RadixArk&;index=1&;md5=aef0b6f234c67fd66a448de36fdd6e9a" target="_blank" rel="nofollow noopener" shape="rect">RadixArk</a>, a company providing access to frontier AI infrastructure, has launched with $100 million in Seed funding at a $400 million post-money valuation. The round was led by Accel and co-led by Spark Capital, with participation from NVentures (NVIDIA’s venture capital arm), Salience Capital, A&;E Investments, HOF Capital, Walden Catalyst Ventures, AMD, LDV Partners, WTT Investment, and MediaTek.</p>
<p>Other investors include Igor Babuschkin (Co-Founder of xAI), Lip-Bu Tan (CEO of Intel), Hock Tan (CEO of Broadcom), John Schulman (Co-Founder of OpenAI and Thinking Machines Lab), Soumith Chintala (PyTorch creator and CTO of Thinking Machines Lab), Olivier Pomel (Co-Founder of Datadog), Thomas Wolf (Co-Founder of Hugging Face), William Fedus (Co-Founder of Periodic Labs), Robert Nishihara (Co-Founder of Anyscale), Eric Zelikman (Co-Founder of humans&;), and Logan Kilpatrick (Gemini Product Lead). The company will use the capital to grow SGLang, accelerate support for emerging model architectures and frontier hardware, and build large-scale inference and training infrastructure for the next generation of AI applications.</p>
<p>RadixArk was founded by Ying Sheng and Banghua Zhu, AI infrastructure and modeling veterans from xAI and NVIDIA. In 2023, Sheng and others created SGLang, an open-source inference engine for serving models at scale. SGLang quickly became a de facto open-source standard, stewarded by a global community of thousands of contributors across hundreds of companies, universities, and research organizations. SGLang is now deployed across hundreds of thousands GPUs worldwide and generates trillions of tokens daily for Google, Microsoft, NVIDIA, Oracle, AMD, Nebius, LinkedIn, xAI, Thinking Machines Lab, and humans&;.</p>
<p>Today, the most sophisticated AI infrastructure is only available to a handful of companies. Neo-labs must rebuild core training and inference stacks from scratch, while infrastructure teams at every company from enterprises to startups are understaffed and underresourced. The result is enormous waste from duplicated effort, siloed research insights, and impeded progress for the entire AI ecosystem. By treating infrastructure as a first-class priority, RadixArk delivers the foundational open systems needed to build the next generation of AI.</p>
<p>“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 everyone can build on them.”</p>
<p>RadixArk will go beyond traditional inference solutions that offer compute access for off-the-shelf or open-source models. Instead, the company is building an end-to-end platform that supports the full lifecycle of model development, including training proprietary models, fine-tuning open models, running reinforcement learning, and deploying and running inference at scale. By standardizing on a single platform, RadixArk customers maintain ownership and control of their models while having access to best-in-class infrastructure primitives.</p>
<p>“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.”</p>
<p>RadixArk’s platform is built on battle-tested, open foundations across the AI stack. Inference runs on SGLang, the fastest and most flexible open engine for modern models, while reinforcement learning is powered by Miles, the company’s open-source framework for large-scale training. SGLang was incubated at LMSys, a nonprofit organization founded by researchers from Stanford, Carnegie Mellon, UC Berkeley, and other universities.</p>

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