Amazon Web Services has scored another major win for its custom AWS Trainium accelerators after striking a deal with AI video startup Decart. The partnership will see Decart optimise its flagship Lucy model on AWS Trainium3 to support real-time video generation, and highlight the growing popularity of AI accelerators over Nvidia’s graphics processing units.
Decart is essentially going all-in on AWS, and as part of the deal, the company will also make its models available through the Amazon Bedrock platform. Developers can integrate Decart’s real-time video generation capabilities into almost any cloud application without worrying about underlying infrastructure.
The distribution through Bedrock increases AWS’s plug-and-play capabilities, demonstrating Amazon’s confidence in growing demand for real-time AI video. It also allows Decart to expand reach and grow adoption among the developer community. AWS Trainium provides Lucy with the extra processing grunt needed to generate high-fidelity video without sacrificing quality or latency.
Custom AI accelerators like Trainium provide an alternative to Nvidia’s GPUs for AI workloads. While Nvidia still dominates the AI market, its GPUs processing the vast majority of AI workloads, it’s facing a growing threat from custom processors.
Why all the fuss over AI accelerators?
AWS Trainium isn’t the only option developers have. Google’s Tensor Processing Unit (TPU) product line and Meta’s Training and Inference Accelerator (MTIA) chips are other examples of custom silicon, each having a similar advantage over Nvidia’s GPUs – their ASIC architecture (Application-Specific Integrated Circuit). As the name suggests, ASIC hardware is engineered specifically to handle one kind of application and do so more efficiently than general purpose processors.
While central processing units are generally considered to be the Swiss Army knife of the computing world due to their ability to handle multiple applications, GPUs are more akin to a powerful electric drill. They’re vastly more powerful than CPUs, designed to process massive amounts of repetitive, parallel computations, making them suitable for AI applications and graphics rendering tasks.
If the GPU is a power drill, the ASIC might be considered a scalpel, designed for extremely precise procedures. When building ASICs, chipmakers strip out all functional units irrelevant to the task for greater efficiency – all their operations are dedicated to the task.
This yields massive performance and energy efficiency benefits compared to GPUs, and may explain their growing popularity. A case in point is Anthropic, which has partnered with AWS on Project Rainier, an enormous cluster made up of hundreds of thousands of Trainium2 processors.
Anthropic says that Project Rainier will provide it with hundreds of exaflops of computing power to run its most advanced AI models, including Claude Opus-4.5.
The AI coding startup Poolside is also using AWS Trainium2 to…
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