Roy Frostig
@froystig.bsky.social
research scientist at google deepmind.
co-author of JAX (https://github.com/jax-ml/jax)
https://cs.stanford.edu/~rfrostig
co-author of JAX (https://github.com/jax-ml/jax)
https://cs.stanford.edu/~rfrostig
Reposted by Roy Frostig
Training our most capable Gemini models relies heavily on our JAX software stack+Google's TPU hardware platforms.
If you want to learn more, see this awesome book "How to Scale Your Model":
jax-ml.github.io/scaling-book/
Put together by several of my Google DeepMind colleagues listed below 🎉.
If you want to learn more, see this awesome book "How to Scale Your Model":
jax-ml.github.io/scaling-book/
Put together by several of my Google DeepMind colleagues listed below 🎉.
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
February 4, 2025 at 7:51 PM
Training our most capable Gemini models relies heavily on our JAX software stack+Google's TPU hardware platforms.
If you want to learn more, see this awesome book "How to Scale Your Model":
jax-ml.github.io/scaling-book/
Put together by several of my Google DeepMind colleagues listed below 🎉.
If you want to learn more, see this awesome book "How to Scale Your Model":
jax-ml.github.io/scaling-book/
Put together by several of my Google DeepMind colleagues listed below 🎉.
Our online book on systems principles of LLM scaling is live at jax-ml.github.io/scaling-book/
We hope that it helps you make the most of your computing resources. Enjoy!
We hope that it helps you make the most of your computing resources. Enjoy!
February 4, 2025 at 6:59 PM
Our online book on systems principles of LLM scaling is live at jax-ml.github.io/scaling-book/
We hope that it helps you make the most of your computing resources. Enjoy!
We hope that it helps you make the most of your computing resources. Enjoy!