RadixArk

SHIP AI FOR ALL.

[RadixArk @radixark]

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.

[RadixArk @radixark]

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

[dstack @dstackai]

The community asked us for an example of how to use @radixark Miles with dstack for RL training.

Since Miles uses Ray and dstack can run Ray, using Miles with dstack is quite straightforward.

Here’s a new example of running Miles on a multi-node cluster provisioned and managed by dstack:

https://t.co/TOxsg8aBLr

[RadixArk @radixark]

@lihanc02 @lsyincs Yes, you are absolutely right! Come and support @lsyincs

[RadixArk @radixark]

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: https://t.co/3EYgii5bV2

[LMSYS Org @lmsysorg]

NYC, we're bringing the inference + finance crowd together for #NYTechWeek @Techweek_!

SGLang Happy Hour: AI Infra in Finance
🕤Wed, June 3 · 6–9 PM ET
���1/2 Bond St, New York

Co-hosted with @HOFCapital, @CrusoeAI, @CloudflareDev, @ArklexAI. Lightning talks from inference engineers and researchers shipping into trading, research, compliance, and risk, followed by an open happy hour for networking. More surprise speakers to be announced — stay tuned 👀

Expected attendees from leading quant funds, banks, and trading firms, including Jane Street, Citadel, Two Sigma, Goldman Sachs, Bloomberg, among others.

We've also got a bartender on site and a full bar. Come have a drink with us!

Limited space. RSVP 👇
https://t.co/M6qaDYYKxF

[RadixArk @radixark]

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♠️

[Jesse Zhang @thejessezhang]

Our first Stacked poker tournament was a huge success! 1 player representing each AI company.

Congrats to:

🥇 Guodong Zhang (RadixArk, co-founder of xAI) @Guodzh
🥈 Jeremy Stribling (Cursor)
🥉 Neal Wu (Thinking Machines) @neal_wu

We will be hosting another one! More below👇

[RadixArk @radixark]

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！

[Junli Wang @JunliWang2021]

Digital agent learning needs massive rollouts. But digital agent rollouts are painfully slow due to heavy environments. 🐌

🚀 We introduce NanoRollout, a lightweight open infra (900 lines core code) for digital agent rollout at scale, validated with three workloads:

🏋️ Large batchsize (4K) SWE Agent RL -> surpasses DeepSWE-32B
🧪 250k+ distilled coding trajectories -> SOTA ≤32B open coding agent
⚡ Fast evaluation on coding/cua/unified agent -> finish

Check our Blog: https://t.co/IBNqqbLqra

[RadixArk @radixark]

@Armanho57248840 We will be sending out platform access in waves. Please bear with us as we process everyone!

[RadixArk @radixark]

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！

[Delta Institute @DeltaInstitutes]

Headed to MLSys 2026?

Come hang out with our friends at @RadixArk, @SGL_Project, and @EssenceVenture on Tuesday!!

[RadixArk @radixark]

Hey everyone, we hear you, and we've updated the post: https://t.co/KSPYt6JJ8K

Our original intent was to give back to the people who supported SGLang, the contributors, the early users, the ones who believed in the project. None of this would exist without you, and this was our way of saying thank you. We're sorry for the confusion it caused.

Thank you for caring enough to speak up, and we're grateful to be on this journey with you. Let's go SGLang!

[RadixArk @radixark]

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 https://t.co/MVDvcvkFGX 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 🧡

[RadixArk @radixark]

$200 FREE CREDIT! We just launched our inference platform for beta testing, and we're giving it to the community first.

⭐ Star SGLang on GitHub (https://t.co/uEeiF4ANRf) + repost this to claim your credits.
→ Limited spots, first come first serve
→ Deadline: May 13, 2025 (AoE)

Every star, every issue filed, every PR reviewed, every question answered in Slack — You built this with us. Thank you for believing in open-source AI infrastructure, in our mission, and in us.

Claim your credits: https://t.co/MVDvcvkFGX

[RadixArk @radixark]

@thatsmeadarsh Thank you for flagging this! We've removed the star requirement entirely. You can find the latest post here: https://t.co/sSkI2NDoga

[RadixArk @radixark]

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 https://t.co/MVDvcvkFGX 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 🧡

[RadixArk @radixark]

We understand why it looks opaque. Credits are granted manually after we verify the repost, which is why it's not instant. We've updated the offer to make it clearer: https://t.co/KSPYt6JJ8K

Thank you for holding us accountable. It genuinely means a lot that the community cares enough to push back 🧡

[RadixArk @radixark]

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 https://t.co/MVDvcvkFGX 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 🧡

[RadixArk @radixark]

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 https://t.co/MVDvcvkFGX 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 🧡

[RadixArk @radixark]

@ramin_m_h @Accel @sparkcapital Thank you Ramin, and thanks for being a lovely partner of us!

[RadixArk @radixark]

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.

[Zhaoran Wang @zhaoran_wang]

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!

[dstack @dstackai]

@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: https://t.co/sgMXRm1Vqb

Slides: https://t.co/QnHFTbmk2t

🧵 2/10

[0xYottascale @0xyottascale]

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.