We frictionlessly trained on AMD GPUs and TPUs with a unified JAX framework. Our goodput for flagship runs went past 90%. @YashVanjani @mjcOhio @alokpathy @pcmonk painstakingly removed obstacles to maximize experimental velocity.

Nano Banana Pro: "Generate a diagram of a two-layer neural network in the style of Stephen Biesty"

Why guess when you can know?

Unbelievable: the famed Berkeley Math Circle is being forced to shut down due to a bureaucratic requirement where a guest lecturer giving an hour long lesson needs to be officially fingerprinted. How is fingerprinting even still a thing in the 21st century? Chancellor Lyons

SGLang now has a pure Jax backend, and it runs natively on TPU!

⛵Marin 32B Base (mantis) is done training! It is the best open-source base model (beating OLMo 2 32B Base) and it’s even close to the best comparably-sized open-weight base models, Gemma 3 27B PT and Qwen 2.5 32B Base. Ranking across 19 benchmarks:

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TPU-style collective matmuls on GPU!

Want to improve GPU compute/comms overlap? We just published a new short tutorial for you! A few small changes to the Pallas:MGPU matmul kernel is all it takes to turn it into an all-gather collective matmul that overlaps NVLINK comms with local compute:

Curious how to write SOTA performance Blackwell matmul kernels using MGPU? We just published a short step-by-step tutorial: https://t.co/XRVX34juEz At each step, we show exactly what (small) changes are necessary to refine the kernel and the final kernel is just under 150 lines.

@jeremyphoward Luckily we have alternatives :) https://t.co/KNy1bqdqxD Just 100 lines without leaving Python and SOTA performance

Kudos to Terry Tao for this: https://t.co/NN818aZCXy

Today we're putting out an update to the JAX TPU book, this time on GPUs. How do GPUs work, especially compared to TPUs? How are they networked? And how does this affect LLM training? 1/n

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So about a month ago, Percy posted a version of this plot of our Marin 32B pretraining run. We got a lot of feedback, both public and private, that the spikes were bad. (This is a thread about how we fixed the spikes. Bear with me. )

Strong recommend for this book and the JAX/TPU docs, even if you are using Torch / GPUs. Clean notation and mental model for some challenging ideas. https://t.co/ypg3jjbWRx https://t.co/HKBSctquKq https://t.co/k7XXc9Eesg

For a rare look into how LLMs are really built, check out @dlwh's retrospective on how we trained the Marin 8B model from scratch (and outperformed Llama 3.1 8B base). It’s an honest account with all the revelations and mistakes we made along our journey. Papers are forced to

What would truly open-source AI look like? Not just open weights, open code/data, but *open development*, where the entire research and development process is public *and* anyone can contribute. We built Marin, an open lab, to fulfill this vision:

$SHNY $DULL ±3X daily resetting GLD-linked ETNs.

Excited to introduce PyRoki ("Python Robot Kinematics"): easier IK, trajectory optimization, motion retargeting... with an open-source toolkit on both CPU and GPU

Pretty awesome result from the new version of Gemini 2.5 I changed one line of War and Peace, inserting a sentence into Book 14, Chapter 10 (halfway through), where Princess Mary "spoke to Crab Man the superhero" Gemini 2.5 consistently found this reference among 860,000 tokens

A thread of thoughts on radiance fields, from my keynote at 3DV: Radiance fields have had 3 distinct generations. First was NeRF: just posenc and a tiny MLP. This was slow to train but worked really well, and it was unusually compressed --- The NeRF was smaller than the images.

Llama 4 inference in pure JAX! Expert/tensor parallelism with int8 quantization. Contributions welcome!

A nice and concise R1 inference jax:tpu port by @rdyro128523. Good for both reading and running. Watch the repo for more.