Sen. Sational. What a weekend. What a win. Is there anyone quite as good as Lewis Hamilton when he’s got his back against the wall? Incredible.

A racer. A winner. A role model. Both on and off the track. And a Champion. Always. 💜

JAX+NVIDIA at #GTC22! w/ Mahmoud Soliman nvidia.com/gtc/session-ca… New to JAX? This talk gets you up to speed. Already a JAXpert? Check out the new parallelization features at the end of the demo. And hear about how NVIDIA is making JAX faster and more scalable than ever on GPUs!

JAX 0.4.0 is coming out soon and it contains some awesome features! 🚀 🧵...

Words to remember from Lewis Hamilton: Failure is 💯necessary for greatness. 🏆

For some of us VSCode binding ⌘F to find for the *file explorer* has broken our zen work flow state (I only ever want to search code with this shortcut, not the file listing). Here’s the fix: superuser.com/questions/1748…

Chandrayaan-3 Mission: 'India🇮🇳, I reached my destination and you too!' : Chandrayaan-3 Chandrayaan-3 has successfully soft-landed on the moon 🌖!. Congratulations, India🇮🇳! #Chandrayaan_3 #Ch3

Matthew Johnson I asked for a cute / TikTok-able edition of the JAX release notes, and now I can't stop laughing 😂

The Gemini era is here. Thrilled to launch Gemini 1.0, our most capable & general AI model. Built to be natively multimodal, it can understand many types of info. Efficient & flexible, it comes in 3 sizes each best-in-class & optimized for different uses blog.google/technology/ai/…

More exciting news today -- Gemini 1.5 Pro result is out! Gemini 1.5 Pro API-0409-preview now achieves #2 on the leaderboard, surpassing #3 GPT4-0125-preview to almost top-1! Gemini shows even stronger performance on longer prompts, in which it ranks joint #1 with the latest

We’re sharing Project Astra: our new project focused on building a future AI assistant that can be truly helpful in everyday life. 🤝 Watch it in action, with two parts - each was captured in a single take, in real time. ↓ #GoogleIO

Gemini and I also got a chance to watch the OpenAI live announcement of gpt4o, using Project Astra! Congrats to the OpenAI team, super impressive work!

Many of you are excited about H100 attention, so it’s a good time to show you Mosaic GPU: a Python DSL for H100s. The attention example matches FA3 performance, while being only ~200 lines of Python: github.com/google/jax/blo… It's easy to install too! Latest JAX packages have it.

Making LLMs run efficiently can feel scary, but scaling isn’t magic, it’s math! We wanted to demystify the “systems view” of LLMs and wrote a little textbook called “How To Scale Your Model” which we’re releasing today. 1/n

Deepseek R1 inference in pure JAX! Currently on TPU, with GPU and distilled models in-progress. Features MLA-style attention, expert/tensor parallelism & int8 quantization. Contributions welcome!

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

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

highly recommend you try out JAX's new Explicit Sharding API. its more intuitive in that for intermediate computation .sharding will print the actual sharding at that point so you don't have to add with_sharding_constraint everywhere, but its a bit more strict. you can

I can attest after using explicit sharding for a couple of months that I feel a deep sense of calm whenever I train models, knowing exactly where all my shards are ahead-of-time.

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