If you're lucky enough to be in Seattle on a clear, sunny day, you'll likely overhear a local say, "the mountain is out." They're referring to Mount Rainier, the 14,410-foot (4,392-meter) stratovolcano that towers above the surrounding terrain.
Rainier’s commanding presence explains why Amazon Web Services (AWS) borrowed its name for a project that similarly dwarfs any comparable endeavor: the creation of what’s expected to be the world's most powerful computer for training artificial intelligence (AI) models.
Project Rainier, announced at the end of last year and now well underway, is one of the company’s most ambitious undertakings to date. It’s a massive, one-of-its-kind machine designed to usher in the next generation of AI.
Spread across multiple data centers in the U.S., the sheer size of the project is unlike anything AWS has ever attempted.
A mountain of compute
AWS customer AI safety and research company Anthropic will use this brand new ‘AI compute cluster’ to build and deploy future versions of its leading AI model, Claude.
“Rainier will provide five times more computing power compared to Anthropic’s current largest training cluster,” said Gadi Hutt, director of product and customer engineering at Annapurna Labs, the specialist chips arm of AWS responsible for designing and building the hardware that will power the project.
“For a frontier model like Claude, the more compute you put into training it, the smarter and more accurate it will be,” said Hutt. “We’re building computational power at a scale that’s never been seen before and we’re doing it with unprecedented speed and agility.”
Chips chips chips
To deliver on that mission, Project Rainier is designed as a massive “EC2 UltraCluster of Trainium2 UltraServers.” The first part refers to Amazon Elastic Compute Cloud (EC2), an AWS service that lets customers rent virtual computers in the cloud rather than buying and maintaining their own physical servers.
The more interesting bit is Trainium2, a custom-designed AWS computer chip built specifically for training AI systems. Unlike the general-purpose chips in your laptop or phone, Trainium2 is specialized for processing the enormous amounts of data required to teach AI models how to complete all manner of different and increasingly complex tasks—fast.
To put the power of Trainium2 in context: a single chip is capable of completing trillions of calculations a second. If, understandably, that’s a little hard to visualize: consider that it would take one person more than 31,700 years to count to one trillion. A task that would require millennia for a human to complete can be done in the blink of an eye with Trainium2.
From traditional to ultra
Impressive, yes. But Project Rainier doesn’t just use one, or even a few, chips. This is where the UltraServers and UltraClusters come in.
Traditionally, servers in a data center operate independently. If and when they need to share information, that data has to travel through external network switches. This introduces latency (i.e, delay), which is not ideal at such large scale.
AWS’s answer to this problem is the UltraServer. A new type of compute solution, an UltraServer combines four physical Trainium2 servers, each with 16 Trainium2 chips. They communicate via specialized high-speed connections called “NeuronLinks.” Identifiable by their distinctive blue cables, NeuronLinks are like dedicated express lanes, allowing data to move much faster within the system and significantly accelerating complex calculations across all 64 chips.
When you connect tens of thousands of these UltraServers and point them all at the same problem, you get Project Rainier—a mega “UltraCluster.”
This is also where you start to understand why Hutt affectionately refers to Rainier as a “friendly giant.”
No room for failure
Communication between components happens at two critical levels: the NeuronLinks provide high-bandwidth connections within UltraServers, while Elastic Fabric Adapter (EFA) networking technology (identified by its yellow cables) connects UltraServers inside and across data centers. This two-tier approach maximizes speed where it's most needed while maintaining the flexibility to scale across multiple data center buildings.
So far, so good—but operating and maintaining such an enormous computer is not without its challenges. To ensure all of that gigantic capacity is available to customers, reliability is paramount. That’s where the company’s approach to hardware and software development really comes to the fore.
Unlike most other cloud providers, AWS builds its own hardware, and in doing so, can control every aspect of the technology stack, from a chip’s tiniest components, to the software that runs on it, to the complete design of the data center itself.
Controlling the stack
This kind of vertical integration is one part of what gives AWS such an advantage in the race to accelerate machine learning and reduce cost barriers to making AI more accessible.
“When you know the full picture, from the chip all the way to the software, to the servers themselves, then you can make optimizations where it makes the most sense,” said Annapurna director of engineering Rami Sinno.
“Sometimes the best solution might be redesigning how power is delivered to the servers, or rewriting the software that coordinates everything. Or it might be doing all of this at once. Because we have an overview of everything, at every level, we can troubleshoot rapidly and innovate much, much faster.”
Sustainability at scale
“The team that engineers our data centers—from rack layouts to electrical distribution to cooling techniques—is continuously increasing energy efficiency,” said Hutt. “Regardless of the scale AWS operates at, we always keep our sustainability goals front of mind.”
When it comes to carbon-free use in data centers, all of the electricity consumed by Amazon’s operations, including its data centers, was matched with 100% renewable energy resources in 2023.
The company is investing billions of dollars in nuclear power and battery storage, and in financing large-scale renewable energy projects around the world to power its operations. In fact, for the past five years Amazon has been the largest corporate purchaser of renewable energy in the world. The company is still on a path to be net-zero carbon by 2040. This goal remains unchanged by the addition of Project Rainier, and its continued worldwide growth in general.
Last year AWS announced it would be rolling out new data center components that combine advances in power, cooling, and hardware, not only for data centers it’s currently building, but also in existing facilities. New data center components are projected to reduce mechanical energy consumption by up to 46% and reduce embodied carbon in the concrete used by 35%.
The new sites the company is constructing to support Project Rainier and beyond will include a variety of upgrades for energy efficiency and sustainability.
These will have a strong focus on water stewardship. AWS engineers its facilities to use as little water as possible, and where possible none at all. One way it does this is by eliminating cooling water use in many of its facilities for most of the year, instead relying on outside air.
For example, data centers in St. Joseph County, Indiana—one of the Project Rainier sites—will maximize the use of outside air for cooling. From October to March the data centers won’t use any water for cooling at all, while on an average day from April to September they’ll only use cooling water for a few hours per day.
Thanks to engineering innovations like this, AWS leads the industry in water efficiency. Based on findings from a recent Lawrence Berkeley National Laboratory report looking at the data center industry’s water usage efficiency, the industry standard measure of how efficiently water is used inside data centers is 0.375 liters of water per kilowatt-hour. At 0.15 liters of water per kilowatt hour, AWS’s rate is more than twice as good as than the industry average. It’s also a 40% improvement since 2021.
The future of AI
Project Rainier doesn't just push technical boundaries—it represents a fundamental shift in what's possible with AI. And the implications go much further than making Claude an infinitely more sophisticated model.
Project Rainier is now a template for deploying the kind of raw computational power that will allow AI to tackle challenges that have long resisted human solution, enabling breakthroughs across everything from medicine to climate science.
Just as its namesake peak stands as a defining landmark of the Pacific Northwest, Project Rainier marks a distinct before-and-after moment in computing history—one that could transform the technological landscape, chip by chip by chip.
More about AWS innovation
- Look inside Annapurna Labs, where AWS designs custom chips.
- Discover how Amazon is making its data centers more sustainable, through hardware and liquid cooling efficiency, reducing carbon emissions, and optimizing workloads.
- Learn about how AWS is already more than 50% of the way to being ‘water positive’ by 2030, meaning it will return more water to communities and the environment than it uses in its data center operations.
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