Periodic Labs' Neon tops GPT-6 Astra using just 1,300 H200s and closed-loop science
BanghuaZ · x · 2026-09-16
Periodic Labs unveiled Neon, an open-source model for materials science trained with mid-training and RL on only 1,300 H200s plus months of proprietary lab data, surpassing GPT-6 Astra on its analysis benchmark.
Key points:
- The team built high-throughput materials labs in Menlo Park to close the loop between experiments and models: labs generate fresh data, models learn from it, then suggest what to try next — targeting hard problems like superconductors, magnets, and semiconductor materials.
- The training stack extends the open-source SGLang and Miles to trillion-parameter-scale scientific RL, with more efficient training, lower memory use, and 2.5x faster inference; the improvements were contributed back to both projects.
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