Amazon.com Inc（AMZN）資訊與事件AMZNAmazon.com Inc

Amazon Secures $5 Billion AI Hardware from Nvidia Early

Amazon (AMZN) is racing to secure AI hardware earlier than before, even as data center power shortages leave some GPU systems waiting in warehouses, Business Insider's Eugene Kim reports. Under a new Early Rack Receipt process, AWS expected to receive roughly $5B in Nvidia's (NVDA) GB300 server racks at U.S. facilities before final deployment, according to August internal emails reviewed by Business Insider. The company projected roughly $3B more in GB300 racks each month "for the foreseeable future," one of the emails said. Some AWS sites had room for more racks but not enough available power to turn them on, people familiar with the effort said, leaving finished equipment to be staged at intermediary warehouses until the facilities were ready.

Amazon Launches TorchNeuron to Aid AI Development

Amazon Web Services stated in a blog post: "Today we launched TorchNeuron, which developers can use to run their existing PyTorch code natively on AWS Trainium, the AI chips we design. Trying a new AI chip used to be a lot of work. You often had to rewrite your code, check that it still gave the right answers, and spend many weeks on it, betting that the new chip was the right one for your workload. That cost time and money, and it wore on teams. Engineers spent that time adapting software instead of improving the product. TorchNeuron helps take that off the table. PyTorch is the software most AI teams use to build and run their models. In most cases, teams can try Trainium with a configuration change rather than a code or model rewrite, and popular libraries like Hugging Face Transformers now support Trainium directly. Combined with Trainium's price-performance, TorchNeuron will make it easier for many more companies to use AI cost-effectively. It also changes what teams can choose... TorchNeuron is open source under the Apache 2.0 license and enters public beta today. We worked closely with the PyTorch team at Meta, and contributed our changes to PyTorch itself, making it easier to run PyTorch on other hardware in the future."