An exclusive tour of Amazon’s Trainium lab, the chip that’s won over Anthropic, OpenAI, even Apple 

The AWS Chip Lab Tour: Inside Amazon’s Cutting-Edge Chip Development Facility

Shortly after Amazon CEO Andy Jassy announced AWS’s groundbreaking $50 billion investment deal with OpenAI, Amazon invited me on a private tour of the chip development lab at the heart of the deal, at (mostly*) its own expense.

Industry experts are watching Amazon’s Trainium chip, created at that facility, for its implications for lower-cost AI inference and, potentially, a dent in Nvidia’s near monopoly.

Curious, I agreed to go.

My tour guides for the day were the lab’s director, Kristopher King (pictured below right) and director of engineering Mark Carroll (below left), as well as the team’s PR person who arranged the visit, Doron Aronson (pictured with yours truly later in the story).

AWS has been Anthropic’s major cloud platform since the AI lab’s early days — a relationship significant enough to survive Anthropic later adding Microsoft as a cloud partner as well, and Amazon’s growing partnership with OpenAI.

The OpenAI deal makes AWS the exclusive provider of the model maker’s new AI agent builder, Frontier, which could become an important part of OpenAI’s business if agents become as big as Silicon Valley thinks they will. We’ll see if that exclusivity stands exactly as announced. The Financial Times reported this week that Microsoft may believe OpenAI’s deal with Amazon violates its own deal with OpenAI, namely with Redmond getting access to all of OpenAI’s models and tech.

What makes AWS so appealing to OpenAI? As part of this deal, the cloud giant has agreed to supply OpenAI with 2 gigawatts of Trainium computing capacity. This is a giant commitment, given that Anthropic and Amazon’s own Bedrock service are already consuming Trainium chips faster than Amazon can produce them.

There are 1.4 million Trainium chips deployed across all three generations, and Anthropic’s Claude runs on over 1 million of the Trainium2 chips deployed, the company said.

It’s worth noting that while Trainium was originally geared toward faster, cheaper model training (a bigger priority a couple of years ago), it’s now tuned and used for inference as well. Inference — the process of actually running an AI model to generate responses — is currently the biggest performance bottleneck in the industry.

Case in point: Trainium2 handles the majority of the inference traffic on Amazon’s Bedrock service, which supports the building of AI applications by Amazon’s many enterprise customers and allows the apps to use multiple models.

“Our customer base is just expanding as fast as we can get capacity out there,” King said. “Bedrock could be as big as EC2 one day,” he added, referring to AWS’s behemoth compute cloud service.

Trainium vs. Nvidia

Beyond offering an alternative to Nvidia’s backlogged, hard-to-acquire GPUs, Amazon says its new chips running on its new specialty Trn3 UltraServers cost up to 50% less to run for comparable performance than using classic cloud servers.

Along with Trainium3, released in December, this AWS team also built new Neuron switches, and Carroll says that combo is transformative.

“What that gives us is something huge,” Carroll said. The switches allow every Trainium3 chip to talk to every other chip in a mesh configuration, reducing latency. “That’s why Trainium3 is breaking all kinds of records,” particularly in “price per power,” he said.

When trillions of tokens a day are involved, such improvements add up.

In fact, Amazon’s chip team was lauded by Apple in 2024. In a rare moment of openness for the secretive company, Apple’s director of AI publicly described how it used another of the team’s chips — Graviton, a low-power, ARM-based server CPU and the first breakout chip this team designed. Apple also lauded Inferentia — a chip specifically designed for inference — and gave a nod to Trainium, which was new at the time.

These chips represent the classic Amazon playbook: See what people want to buy, then build an in-house alternative that competes on price.

The catch for chips, historically, has been switching costs. Applications written for Nvidia’s chips must be re-architected to work with others — a time-consuming process that discourages developers from switching.

But the AWS chip team proudly told me that Trainium now supports PyTorch, a popular open source framework for building AI models. That includes many of the ones hosted on Hugging Face, a vast library where developers share open source models.

The transition, Carroll told me, requires “basically a one-line change, and then recompile, and then run on Trainium.” In other words, Amazon is attempting to chip away at Nvidia’s market dominance wherever possible.

AWS has also this month announced a partnership with Cerebras Systems, integrating that company’s inference chip on servers running Trainium for what Amazon promises will be superpowered, low-latency AI performance.

But Amazon’s ambitions go beyond the chips themselves. It also designs the server that hosts the chips. Besides the networking components, this team has designed “Nitro,” a hardware-software combo that provides virtualization tech (which allows many instances of software to run separately on the same server); new state-of-the-art liquid cooling technology; and the server sleds (pictured below) that host this gear.

All of that is to control cost and performance.

Working 24/7 on the “bring-up”

Amazon’s custom chip-designing unit was born when the cloud giant bought Israeli chip designer Annapurna Labs in January 2015 for about $350 million. So this team has now had more than 10 years designing chips for AWS. The unit has retained its Annapurna roots and name — its logo is everywhere in the office.

This chip lab is located in a shiny, chrome-windowed building in Austin’s upscale “The Domain” district, a walkable area filled with shops and restaurants that’s sometimes called Austin’s Silicon Valley.

The offices have your classic tech corporate vibe: desks in cubicles, gathering spots, and conference rooms. But tucked away at the back of a high floor in the building is the actual lab, with sweeping views of the city.

The shelving-filled lab, about the size of two large conference rooms, is a noisy industrial space thanks to the fans on the equipment. It looks like a cross between a high school shop class and a Hollywood set for a high-end lab, except the engineers are dressed in jeans, not white lab coats.

The star of the lab is an entire row showcasing each generation of the “sleds” the team designed. Sleds are the trays that house the Trainium AI chips, Graviton CPU chips, and supporting boards and components. Stack them together on a rack with the networking component, also custom-designed by this team, and you get the systems that are at the heart of Anthropic Claude’s success.

At this data center, there are rows and rows of servers filled with sleds that integrate all of Amazon’s newest custom chips: Graviton CPU, liquid-cooled Trainium3, Amazon Nitro, all happily computing away. The liquid runs on a closed system, meaning it is reused, which should also help reduce the environmental impact, the engineers said.

While attention on the team has always been high, the scrutiny has really ratcheted up as of late.

Amazon CEO Andy Jassy keeps a close eye on this lab, publicly bragging about its products like a proud dad. In December, he said Trainium was already a multibillion-dollar business for AWS and called it one piece of AWS tech he’s most excited about. He also gave the chip a shout-out when announcing the OpenAI agreement.

The team feels the pressure, too. Engineers will work 24/7 for three to four weeks around each bring-up event to fix any issues so the chips can be mass-produced and put into data centers.

“It’s very important that we get as fast as possible to prove that it’s actually going to work,” Carroll said. “So far, we’ve been doing really well.”

*Disclosure: Amazon provided airfare and covered the cost of one night at a local hotel. Honoring its Leadership Principle of Frugality, this was a back-of-the-plane middle seat and a modest room. DailyTech picked up the other associated travel costs like Ubers and luggage fees. (Yes, I checked a bag for an overnight trip. I’m high maintenance that way.)