Amazon Web Services launched its in-house-built Trainium3 AI chip on Tuesday, marking a significant push to compete with Nvidia in the lucrative market for artificial intelligence computing power.
The move intensifies competition in the AI chip market, where Nvidia currently dominates with an estimated 80- to 90-percent market share for products used in training large language models that power the likes of ChatGPT.
Google last week caused tremors in the industry when it was reported that Facebook parent Meta would employ Google AI chips in data centers, signaling new competition for Nvidia, currently the world’s most valuable company and a bellwether for the AI investment frenzy.
This followed the release of Google’s latest AI model last month that was trained using the company’s own in-house chips, not Nvidia’s.
AWS, which will make the technology available to its cloud computing clients, said its new chip delivers over four times the computing performance of its predecessor while using 40 percent less energy.
Energy consumption is one of the major concerns about the AI revolution, with major tech companies having to scale back or pause their net-zero emissions commitments as they race to keep up on AI.
“Training cutting-edge models now requires infrastructure investments that only a handful of organizations can afford,” AWS said, positioning Trainium3 as a way to democratize access to high-powered AI computing.
AWS said several companies are already using the technology, including Anthropic, maker of the Claude AI assistant and a competitor to OpenAI, the maker of ChatGPT.
AWS also announced it is already developing Trainium4, expected to deliver at least three times the performance of Trainium3 for standard AI workloads.
The next-generation chip will support Nvidia’s technology, allowing it to work alongside that company’s servers and hardware.
Amazon’s in-house chip development reflects a broader trend among cloud providers seeking to reduce dependence on external suppliers while offering customers more cost-effective alternatives for AI workloads.
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