Periodic Labs chose SGLang and Miles to build Neon, an open-source model it says surpasses GPT-6 Astra on its analysis benchmark after mid-training and RL on 1,300 H200s.
RadixArk says Periodic extended both frameworks for scientific RL at trillion-parameter scale, delivering more efficient training, lower memory use, and 2.5x faster inference. The work has been contributed back to both projects.
We're proud that @periodiclabs chose SGLang and Miles to build Neon.
Periodic extended SGLang and Miles to run scientific RL at trillion-parameter scale, with more efficient training, lower memory use, and 2.5x faster inference. This work was contributed back to both projects.
Excited to see where teams take SGLang and Miles next.
We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next. Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon. This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials. Read our blog posts below.View quoted post on X
Source: RadixArk · x.comPublished · added here