Andy Jassy

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Several years ago, when we started pursuing building our own chips, a lot of folks thought this was nuts. We heard a lot of the same refrains you often hear—why make this investment, why invest in a team and all the other fixed costs to develop your own chip when you can buy from other suppliers? And, while we knew we’d partner with those other companies for the foreseeable future, if your customers are telling you they’re thirsty for better price-performance, and you’re driven by what makes customers’ lives better and easier every day, you explore options to make it so.

We realized pretty quickly that designing our own chips was going to be the best path to delivering this value for customers. We were lucky to find and join forces with the amazing Annapurna Labs team, who started with a chip (named Nitro) that offloaded security, networking, and some other virtualization functions from our servers so customers could use more of the server than they could before. Then, that team built a generalized CPU chip, Graviton, which has been very popular and impactful for customers, before embarking on building custom AI chips—Trainium (for training) and Inferentia (for inference)—which are also off to a strong start.

Am very excited about our most recent chip releases at AWS re: Invent: Graviton4 and Trainium2. Graviton4 marks the fourth generation we’ve delivered in just five years (you can see the evolution from left to right in the image below), and it’s the most powerful and energy efficient multipurpose chip we have built to date. And with the surge of interest in generative AI, Trainium2 will help customers train their ML models faster, at a more advantaged price-performance. I’m really proud of the pace of innovation our teams are delivering on and what it is making possible for customers!

Earlier this year, some developers created an internal tool for Amazonians to experiment with Foundation Models from Amazon Bedrock, and it quickly became a word-of-mouth hit across AWS.

Because the interface only required a user to add in a description of the app they wanted to build in a prompt box, the tool could be used by teammates across a wide spectrum of roles and technical expertise.

Some built apps for productivity, while others made ones for amusement, or that tapped into personal passions. It was cool to see builders get inspired by using generative AI in a more hands on way, and by sharing their apps with each other, they were actually sharing key generative AI techniques in action.

Based on the popularity and feedback, we launched this simple to use but powerful app to the world for free, naming it PartyRock (in reference to it being a fun and collaborative way to experience Amazon Bedrock). It is built on the belief that everyone should have access to a fun and easy tool to help get started with generative AI.

Since launching it last week, tens of thousands of apps have been built and shared, and the creativity has been really cool to see (including apps to organize your re:Invent schedule, plan a Formula 1 trip, and find the best coffee in Vegas).

I encourage you to give it a try and see what you can build!