We now are working with models that have very large parameter counts. And too many parameters to store in their memory. So right away they need to shard your parameters across many GPUs. That's a complicated problem. The second problem is the, the actual calculation of a big layer is too big for a GPU. You're trying to think about how to place work on each individual one. Those are painful problems. In our architecture, enough memory for trillions of parameters. We have enough, enough cores, 850,000 cores so that even the largest layer of the largest neural network can fit and we never have to break them. It vastly simplifies how you place big neural networks onto our compute

Get the Snipd
podcast app

Unlock the knowledge in podcasts with the podcast player of the future.
App store bannerPlay store banner

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode

Save any
moment

Hear something you like? Tap your headphones to save it with AI-generated key takeaways

Share
& Export

Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more

AI-powered
podcast player

Listen to all your favourite podcasts with AI-powered features

Discover
highlights

Listen to the best highlights from the podcasts you love and dive into the full episode