
prev @xAI | KubeRay maintainer | Ray committer | Apache Submarine PMC
CA, USA
Joined May 2020
- Tweets110
- Following454
- Followers489
- Likes2K
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.
Work with Xudong! It’s a good chance if you are interested in distributed system and system reliability!
I'm recruiting PhD students at the University of Toronto ECE. Our group aims to develop practical verification techniques for real-world, large-scale cloud systems. Apply to @eceuoft if you are interested in cloud systems, formal verification, or software testing.
1
6
1,588
Grokipedia is closer to the truth by fixing errors, providing more context, and reducing bias. Please give it a try!
The goal of Grok and Grokipedia.com is the truth, the whole truth and nothing but the truth.
We will never be perfect, but we shall nonetheless strive towards that goal.
2
650
Kai-Hsun Chen retweeted
KubeRay is evolving for a better user experience. At Ray Summit this year, Rueian from @anyscalecompute and Andrew (@Google) will share the latest capabilities that make running Ray workloads on @kubernetesio simpler, more reliable, and production-ready.
What you will learn:
- Major Ray Job enhancements: deletion policies, cron scheduling, sidecar mode, background status checks
- Expanded Kubernetes ecosystem integrations: kubectl plugin updates, APIServer V2, KubeRay Metrics, in-place pod resizing
Nov 3–5 at the San Francisco Marriott Marquis.
Register now: na2.hubs.ly/H01rjD60
Kai-Hsun Chen retweeted
🚨 Leaderboard Disrupted!
Grok-4-fast by @xAI has arrived in the Arena, and it’s shaking things up! ⚡️
🏆 #1 on the Search Leaderboard
Tested under the codename “menlo,” Grok-4-fast-search just rocketed to the top spot with the community.
💠 Tied for #8 on the Text Leaderboard
After debuting as “tahoe” in pre-release, Grok-4-fast is officially in the Top 10 - no small feat in the most competitive Arena, particularly for a model in this weight class.
👏 Congrats to the @xAI team on these achievements. See thread for more highlights about Grok-4-fast 🧵
Introducing Grok 4 Fast, a multimodal reasoning model with a 2M context window that sets a new standard for cost-efficient intelligence.
Available for free on grok.com, grok.x.com, iOS and Android apps, and OpenRouter.
x.ai/news/grok-4-fast
Kai-Hsun Chen retweeted
New! Check out how to deploy Frontier LLMs such as @deepseek_ai R1 in a multi-node GPU cluster on @kubernetesio
Inside this blog, you have access to detailed guides to do this in two modes:
🔹Managed Ray on Anyscale -- LLM deployment with highest resilience, seamless scaling and advanced observability all inside your account via bring-your-own-cloud (BYOC).
🔹Self-Managed Ray on GKE -- Customize deployment to your needs, leveraging the scale of Google Kubernetes Engine
Learn more: anyscale.com/blog/deepseek-v…
KubeRay community is lucky to have @davidxia_. He opens PRs to improve KubeRay whenever he encounters pain points as a user, provides feedback, and delivers extremely high-quality code. Thank you for your contributions!
Thanks to @raydistributed, @hsun_kai , and KubeRay maintainers for the nice gift for contributors.
3
381
Kai-Hsun Chen retweeted
This release is special.
We've been working on multi-turn RL for the past few months - with SkyRL-Agent trained on a SweBench-like task and SkyRL-SQL on Text-to-SQL. There was always this friction point while working with existing training frameworks. Often the no. 1 priority with existing frameworks is performance (which is of course important), but this is not the sole thing users care about. In a fast-moving field, being able to prototype your ideas quickly is critical.
Having been in the weeds ourselves, we decided to make flexibility the no. 1 priority for our training framework. Changing your highest priority leads to a night-and-day difference in how your design looks like. SkyRL-v0.1 is us trying to materialize our thoughts on what a good training framework should look like for the future of multi-turn and agentic RL.
Btw, we believe that we can still have great performance (our no. 2 priority). In fact I would say we have a lot of features needed here already (efficient colocated training, asynchronous rollouts, seq parallelism, a lot more that doesn't fit here) but a modular design will enable us to bring the best features in.
If you're working on post-training LLMs, do try it out! (and rip out anything you don't like 😉)
✨Release: We upgraded SkyRL into a highly-modular, performant RL framework for training LLMs. We prioritized modularity—easily prototype new algorithms, environments, and training logic with minimal overhead.
🧵👇
Blog: novasky-ai.notion.site/skyrl…
Code: github.com/NovaSky-AI/SkyRL
Happy to see more big players investing in Ray/KubeRay!
Alibaba Cloud leverages Ray to power large-scale ML workloads on ACK – from training and inference to multi-tenant scheduling and full-stack observability.
Learn more here: anyscale.com/blog/ray-on-ali…
Kai-Hsun Chen retweeted
I’m hosting the Ray + AI Infra Summit, our first-ever summit in New York. Our focus is on highly-curated and deeply technical content. We’ve got an amazing group of speakers who run AI infra at a range of companies sharing their lessons. Among others, you’ll hear from
— Ion Stoica, Co-founder @ Anyscale & creator of Spark, Ray, vLLM
— Jasjeet Sekhon, Chief Scientist @ Bridgewater
— Mike Schuster, Head of AI @ Two Sigma
— Mike Seid, ML Platform Lead @ Spotify
— Christian Stano & Eric Jeske @ Attentive
— Hiroto Udagawa @ Nubank
— Sam Bean @ Character.ai
— Matthieu Mouminoux & Arthur Delaitre @ Mirakl
— Tommy MacWilliam @ Figma
Register at anyscale.com/ray-on-the-road!
Benchmarking is hard. Is performance really that important?
Do users always choose the fastest option, or the one that’s easier to use and operate, as long as the performance meets their needs? In my opinion, it’s the latter. For example: YARN/K8s, Pig/Hive, TF/PyTorch.
This quoted post is unavailable.
2
179
Kai-Hsun Chen retweeted
Introducing the Ray Kubectl Plugin (Beta)! 🙌
Now, managing @raydistributed clusters on @kubernetesio is faster and more efficient.
Key enhancements:
🔹Streamlined workflows - Run your Ray scripts from your laptop on a remote K8s cluster with a single command.
🔹Create Ray clusters & worker groups with a single command, no YAML needed.
Read more about streamlining your Ray cluster management on Kubernetes here: anyscale.com/blog/introducin…
Kai-Hsun Chen retweeted
KubeRay v1.3.0 is here with major upgrades for running @raydistributed on @kubernetesio. 🚀
Key highlights:
⚡️Enhanced observability: The new RayService Conditions API and RayCluster Conditions API have reached Beta, along with the Kubernetes Events API for better tracking and monitoring.
⚡️Stronger reliability: Improved failure recovery, better resource management for more stable Ray clusters, and a doubling of end-to-end tests from 23 to 46.
⚡️Improved usability: Ray Kubectl plugin (Beta) simplifies job and cluster management, along with a new unified GCS fault tolerance API.
KubeRay v1.3.0 improves observability, reliability, and usability making Ray on Kubernetes easier to monitor, recover, and manage. 🙌
Read full blog article here: anyscale.com/blog/kuberay-v1…
Kai-Hsun Chen retweeted
The progress in AI is down to three basic resources; (1) people (experts), (2) data, and (3) infrastructure. Arguably, at this point the US is only ahead in (3). Also, at this point the Chinese open source models are ahead. Not only DeepSeek but also Qwen. This is a fact.
Happy to see more and more post training libraries built on Ray (OpenRLHF, ChatLearn, veRL)!
Introducing veRL by Bytedance! Flexible & efficient framework for LLM RL/RLHF.
Ray-based hybrid programming model. 1.5x - 20x throughput boost.
Kudos to @PeterSheng66 and team, & @raydistributed
Code: github.com/volcengine/verl
Paper: arxiv.org/pdf/2409.19256
#veRL #RL #LLMs
3
375
Kai-Hsun Chen retweeted
Join @hsun_kai and the @anyscalecompute Team on Tues 10/29 for our next Ray Meetup! 🤝
We'll be talking all things distributed computing and how it's being applied in real-world AI projects. Whether you're new to this or have some experience, there's something for everyone.
Don't miss out—sign up here: lu.ma/biporq36
#RayMeetup #AI #Anyscale #Ray
Kai-Hsun Chen retweeted
In this episode, guest host and AI correspondent @moficodes interviews @richliaw and @hsun_kai from @anyscalecompute about Ray and KubeRay. Join us to learn how this open source project enables AI workloads on #Kubernetes!
kubernetespodcast.com/episod…

























