Today, we are thrilled to officially launch RadixArk with $100M in Seed funding at a $400M valuation. The round was led by @Accel and co-led by @sparkcapital. RadixArk exists to make frontier AI infrastructure open and accessible to everyone. Today, the systems behind the most capable AI models are concentrated in a small number of companies. As a result, most AI teams are forced to rebuild training and inference stacks from scratch, duplicating the same infrastructure work instead of focusing on new models, products, and ideas. RadixArk was founded to change that. We are building an AI platform that makes it easier for teams to train and serve the best models at scale. RadixArk comes from the open-source community. We started with SGLang, where many of us are core developers and maintainers, and expanded our work to Miles for large-scale RL and post-training. We will continue contributing to both projects and working with the community to make them the strongest open-source infrastructure foundations for frontier AI. We would like to thank our long-term partners, contributors, and the broader SGLang community for believing in this mission. We're also grateful to @Accel and @sparkcapital, NVentures (Venture capital arm of @nvidia), Salience Capital, A&E Investment, @HOFCapital, @walden_catalyst, @AMD, LDVP, WTT Fubon Family, @MediaTek, Vocal Ventures, @Sky9Capital and our angel investors @ibab, @LipBuTan1, Hock Tan, @johnschulman2, @soumithchintala, @lilianweng, @oliveur, @Thom_Wolf, @LiamFedus, @robertnishihara, @ericzelikman, @OfficialLoganK, and @multiply_matrix among others. Thanks for the exclusive interview with @MeghanBobrowsky at @WSJ about our vision.

Thanks @dstackai for this end-to-end example using Miles with dstack for RL training. Try it and let us know what you build. 👇

Join us in NYC on June 3rd during #NYTechWeek @Techweek_ Liangsheng Yin (@lsyincs) and Mao Cheng (@MCheng89333), both MTS at RadixArk, will present SGLang & Miles, diving into inference infrastructure for finance. RSVP: partiful.com/e/p74X9KDrgoLaD…

When @Guodzh shows up, RadixArk doesn't fold🫡 Thanks @Accel and @DecagonAI for hosting a great night, and thanks @Guodzh for representing us so well! Ready for the next round♠️

Slow, heavy environments have been the real bottleneck for agentic RL. NanoRollout tackles it head-on with a clean rollout-as-a-service design, integrated with miles for scalable agent RL. Great work from the team！

Headed to MLSys 2026? Come hang out with our friends at @RadixArk, @SGL_Project, and @EssenceVenture on Tuesday!!

Last week, we launched the RadixArk platform for beta testing and offered $200 credits to SGLang supporters who helped spread the word. A huge thank you to everyone who signed up and reposted. The response has been incredible. We're working hard to get everyone set up, and we appreciate your patience while we work through the queue. Here's what's coming: ✅ Private Beta access rolling out in waves ✅ $200 in inference credits, pre-linked to your waitlist email Credits will be available in your account as soon as your platform invite arrives. Thanks for all the miles. Stay tuned for what comes next！

We've heard the community's feedback. Our intent was to make sure the credits reached the people who supported SGLang along the way, and we couldn't be here without you. We're updating the offer to better reflect that. RadixArk's platform is open for beta, and we're offering $200 in compute credits to get you started → Sign up at platform.radixark.com and repost this so we can get you set up. → Limited spots, first come first serve. Open through May 13, 2026 (AoE). → Credits will be granted after we verify the repost. (If you already reposted our earlier announcement, that counts too; no need to do it again.) And if SGLang has been useful in your work, consider giving it a star on GitHub. It's a small gesture that means a lot to the people maintaining it. We're in this together, and we're grateful to be building it with you 🧡

SGLang is GOAT. My students and I burdened @GenAI_is_real and colleagues with numerous naive questions. (They answered all of them for free. ^_^) Excited to see them take off!

@thu_yushengsu, the founding member at @radixark and a core contributor to @lmsysorg SGLang, gave a talk on the efficiency and determinism in large-scale RL training using the Miles framework. GitHub repo: github.com/radixark/miles Slides: docs.google.com/presentation… 🧵 2/10

Congrats — huge milestone. Strong thesis. We're attacking it from a different angle at Yotta Labs: inference optimization as a multi-silicon systems problem. Hardware is one variable. Orchestration is the bigger one. Excited to see more teams pushing the frontier here.

We @Accel are excited to be leading @RadixArk’s $100M seed round alongside our friends at @sparkcapital. It’s been a privilege to partner with @ying11231 and @BanghuaZ since inception and to support the incredible @lmsysorg community. Intelligence is no longer the bottleneck! Developers are now constrained by their ability to control, adapt, and reliably serve a growing diversity of AI models across hardware, environments, and use cases at scale. This shift creates an opportunity to build new foundational infrastructure for training and inference. RadixArk’s mission is to build that open infrastructure, and we’re excited to be their partners on the journey.

Very honored to be part of @radixark and begin this incredible journey. The great age of AI has begun. The fastest systems, the frontier intelligence, the future of open infrastructure for AI — we’re building it all and placing it out there for the world. Now, come and claim your One Piece.

Congrats on the launch! SGLang's vision is more ambitious than anyone can imagine

Reliable, efficient, and correct AI infrastructure has been one of the biggest challenges in creating Periodic. That’s why I’m excited to see RadixArk bring serious capital to open-source infrastructure like SGLang. When that layer gets stronger in the open, companies like Periodic can move faster, and the new wave of companies can take on ambitious work that used to require a large-scale infra team from day one.

I was lucky to meet @ying11231 about 1.5 years ago, fairly early in my career. To me, Ying is a visionary, a mentor, and a friend. LLM inference wasn't really a hot topic back when Ying first invited me to work on SGLang — most people were still hesitant to host their own models, and there was ongoing debate about whether RAG plus in-house models could just solve everything. I still vividly remember our first conversation at the LinkedIn campus, where Ying told me that a great RL framework would be essential for the future — and this was even before DeepSeek R1 came out. It's wild to think that today, SGLang and Miles power every corner of the AI world — coding, chat, science, games, you name it. The RadixArk team is one of the most talent-dense, hard-working, and mission-driven teams I've ever come across. They tirelessly squash every bubble they can find and aren't afraid to aggressively refactor large parts of the codebase, no matter how painful (credits to @lm_zheng @lsyincs for shaping the best engineering principles in SGLang). Inference is the present, RL is the future — and @radixark sits at the critical path of both. Huge congrats to the RadixArk team on the seed round — excited to keep building the best AI frameworks together from @periodiclabs!