The U.S.–China AI race won’t be decided by who builds the most datacenters, but by who deploys the most intelligence. We call this Gross Domestic Intelligence (GDI): intelligence per watt × usable power. If the U.S. activates its dense installed base of local AI accelerators in

It’s been exciting watching this work shape up over the past few months, this is important research

My lab is presenting several papers at NeurIPS this week. Swing by the posters below to learn about our work! 🧵

Scaling scientific world models requires co-designing architectures, training objectives, and numerics. Today, we share the first posts in our series on low-precision pretraining, starting with NVIDIA's NVFP4 recipe for stable 4-bit training. Part 1: radicalnumerics.ai/blog/nvfp4-par… Part

Announcing Flapping Airplanes! We’ve raised $180M from GV, Sequoia, and Index to assemble a new guard in AI: one that imagines a world where models can think at human level without ingesting half the internet.

at long last, the final paper of my phd 🧮 Learning to Reason in 13 Parameters 🧮 we develop TinyLoRA, a new ft method. with TinyLoRA + RL, models learn well with dozens or hundreds of params example: we use only 13 parameters to train 7B Qwen model from 76 to 91% on GSM8K 🤯

Data mixing - determining ratios across your training datasets - matters a lot for model quality. While building Olmo 3, we learned it’s hard to set up a method that finds a strong mix, and hard to maintain that mix as datasets change throughout development. Introducing Olmix👇

(1/7) We're releasing ThunderKittens 2.0! Faster kernels, cleaner code, industry contributions, and new state-of-the-art BF16 / MXFP8 / NVFP4 GEMMs that match or surpass cuBLAS! Alongside this release, we’re equally excited to share some insights we learned while squeezing every