ETtech Explainer: Amazon's latest Tranium chip and what it means for the race

Amazon Web Services (AWS) has spent years creating its own AI training hardware, and its newest release, Trainium3, is touted to perform far better than earlier versions.

At its annual tech conference, re:Invent 2025, AWS introduced the Trainium3 UltraServer. This machine runs on the company’s advanced 3-nanometre Trainium3 chip and uses AWS’s own networking systems to support large-scale AI workloads.

What’s special about Trainium3?


AWS says the new system is over four times faster and offers four times more memory. It is built for both training and running demanding AI applications.

Thousands of UltraServers can be connected to provide an application with as many as one million Trainium3 chips, ten times more than before. Each UltraServer contains 144 chips.

The company also says the chips use around 40% less energy than the previous generation. As global data-centre power use continues to climb, AWS is aiming to build systems that need less electricity, not more.

“AWS Trainium is a family of purpose-built AI accelerators — Trn1, Trn2, and Trn3 — designed to deliver scalable performance and cost efficiency for training and inference across a broad range of generative AI workloads,” the company said in a blog post.

Customers such as Anthropic, Japan’s LLM Karakuri, SplashMusic, and Decart are already using Trainium3. According to TechCrunch, they have seen notable reductions in inference costs.

Next up: Trainium4

At AWS’ conference, the company also gave a first look at its next AI chip, Trainium4, which is already in development.

AWS said the new chip will use a technology called "NVLink Fusion". This allows very fast communication between different chips and is one of Nvidia’s key innovations. Nvidia has been encouraging other chip makers to use NVLink, and AWS now joins Intel and Qualcomm in adopting it.

Trainium4 is expected to deliver a big performance boost and support NVLink Fusion, letting the system work smoothly with Nvidia GPUs. At the same time, it will continue to use Amazon’s more affordable server rack design.

The technology will also help AWS build larger AI servers that can communicate efficiently, which is important when training huge AI models. Through this partnership, customers will gain access to AWS’s ‘AI Factories’, private AI infrastructure in their own data centres, for faster performance and readiness.

However, Amazon has not announced a release date for Trainium4. If it follows previous patterns, more details may appear at next year’s conference.

What this means

These new chips could make AWS a more attractive option for large businesses seeking to train and deploy advanced models. Early tests suggest Trainium3 can cut costs by up to 50% compared to traditional GPU-based systems, making large-scale AI more affordable.

Nvidia has long dominated top-tier AI hardware, but Trainium3 gives Amazon a strong alternative that is cheaper while still delivering high performance. By developing its own chips, AWS reduces its reliance on external supplies, such as Nvidia GPUs, for major AI projects.

This, in turn, means that Amazon gains more control over pricing and availability and can offer customers a lower-cost option without sacrificing capability, strengthening its position in the cloud AI market.

Investors responded quickly as well—the rise in Amazon’s share price reflects confidence in its AI strategy and expectations of continued cloud growth. If Trainium3 sees wide adoption, analysts believe it will support stronger profits and reinforce Amazon’s reputation as a leading technology company.