Hanming Lu

[LMSYS Org @lmsysorg]

Inkling, @thinkymachines' first open model, dropped today: 975B total / 41B active MoE, up to 1M context, reasoning natively over text, images, and audio.

Serving and RL support are already live: you can run and shape it on an open stack, starting now.

Day 0 support on SGLang @sgl_project and Miles @radixark👇

- Inkling's new architecture (ShortConv, attention with relative positional embedding, shared expert sink MoE) is natively implemented and deeply optimized, with prefill full CUDA graph and MXFP8 KV cache

- Full parameter and LoRA RL in a customized Megatron backend, train inference consistency via customized kernels, routing replay, and cross-runtime parameter synchronization

- DFlash speculative decoding from @modal for low-latency serving

Launching now, blog and cookbook in the comments ⬇️

[Nasdaq @Nasdaq]

"And that's what SpaceX is all about - is to take the fiction out of science fiction and create an exciting, inspiring future for everyone."

@SpaceX Founder, CEO, CTO & Chairman @elonmusk takes the @NasdaqExchange podium on $SPCX IPO day.

[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.

[Hanming Lu @hanming_lu]

@Guodzh Thank you for everything!

[Hanming Lu @hanming_lu]

@hyhieu226 @OpenAI @xai Take care Hieu 🫡

[SpaceXAI @SpaceXAI]

Since xAI was formed just 30 months ago, the small and talented team has made remarkable progress.

The future has never looked more exciting!

[SpaceXAI @SpaceXAI]

One Team 🚀
https://t.co/8RWbk5jQIQ

[SpaceXAI @SpaceXAI]

https://t.co/RzNuclNfsR

[Ying Sheng @ying11231]

We've been running @radixark for a few months, started by many core developers in SGLang @lmsysorg and its extended ecosystem (slime @slime_framework , AReaL @jxwuyi). I left @xai in August — a place where I built deep emotions and countless beautiful memories. It was the best place I’ve ever worked, the place I watched grow from a few dozen people to hundreds, and it truly felt like home. What pushed me to make such a hard decision is the momentum of building SGLang open source and the mission of creating an ambitious future, within an open spirit that I learnt from my first job at @databricks after my PhD.

We started SGLang in the summer of 2023 and made it public in January 2024. Over the past 2 years, hundreds of people have made great efforts to get to where they are today. We experienced several waves of growth after its first release. I still remember the many dark nights in the summer of 2024, I spent with @lm_zheng , @lsyincs , and @zhyncs42 debugging, while @ispobaoke single-handedly took on DeepSeek inference optimizations, seeing @GenAI_is_real and the community strike team tag-teaming on-call shifts non-stop. There are so many more who have joined that I'm out of space to call out, but they're recorded on the GitHub contributor list forever. The demands grow exponentially, and we have been pushed to make it a dedicated effort supported by RadixArk. It’s the step-by-step journey of a thousand miles that has carried us here today, and the same relentless Long March that will lead us into the tens of thousands of miles yet to come.

The story never stops growing. Over the past year, we’ve seen something very clear:
The world is full of people eager to build AI, but the infrastructure that makes it possible is not shared. The most advanced inference and training stacks live inside a few companies. Everyone else is forced to rebuild the same schedulers, compilers, serving engines, and training pipelines again and again — often under enormous pressure, with lots of duplicated effort and wasted insight.

RadixArk was born to change that. Today, we’re building an infrastructure-first, deep-tech company with a simple and ambitious mission:
"Make frontier-level AI infrastructure open and accessible to everyone."

If the two values below resonate with you, come talk to us:

(1) Engineering as an art.
Infrastructure is a first-class citizen in RadixArk. We care about elegant design and code that lasts. Beneath every line of code lies the soul of the engineer who wrote it.

(2) A belief in openness.
We share what we build. We bet on long-term compounding through community, contribution, and giving more than we take.

A product is defined by its users, yet it truly comes alive the moment functionality transcends mere utility and begins to embody aesthetics.

Thanks to all the miles (the name of our first released RL framework; see below).

https://t.co/2vio4Eiiac

[SpaceXAI @SpaceXAI]

Introducing Grok 4.1, a frontier model that sets a new standard for conversational intelligence, emotional understanding, and real-world helpfulness.

Grok 4.1 is available for free on https://t.co/AnXpIEOPEb, https://t.co/53pltyq3a4 and our mobile apps.

https://t.co/Cdmv5CqSrb

[Toby Pohlen @TobyPhln]

Our official Grok 4 blog post

https://t.co/rLCYUsJJ8S

[Hanming Lu @hanming_lu]

Grok4 🚀
https://t.co/58pKCYbVpT

[Vals AI @ValsAI]

Grok 3 Beta dominates on our proprietary benchmarks, setting the new SOTA on our Finance, Legal and Tax benchmarks.

Congrats @xai @grok @elonmusk 🚀🚀🚀

We just released the benchmark results for xAI's new models: Grok 3 Beta & Grok 3 Mini Fast Beta (High & Low Reasoning) – this is what we found👇 (1/6)

[Lianmin Zheng @lm_zheng]

People have been wondering which model will be the first to reach a 1400 Elo score; no one believed it would be Grok one year ago, but @xAI made it happen!

[Elon Musk @elonmusk]

Grok 3 release with live demo on Monday night at 8pm PT.

Smartest AI on Earth.

[SpaceXAI @SpaceXAI]

ITS HERE: xAI’s brand-new standalone Grok iOS app.

Harness powerful AI, generate stunning images, and login with X to personalize your experience with real time news, sports and local data.

Download now in the US:

https://t.co/5Ejdqh8a34

[NVIDIA AI @NVIDIAAI]

Today at #RaySummit, NVIDIA and @anyscalecompute announced they are partnering to bring NVIDIA AI to Ray open source and the Anyscale Platform, helping developers build, tune, train and scale production #LLMs. Learn more. https://t.co/rUVVugLmyj

[NVIDIA AI @NVIDIAAI]

NVIDIA and @Anyscale have teamed up to accelerate #LLM development. Discover how easy it is to build an LLM-based copilot starting with Ray and NVIDIA AI solutions like TensorRT-LLM and deploy with Anyscale Platform and NVIDIA AI Enterprise. https://t.co/eTHVWYZAci