Amazon is building one of the world's most powerful artificial intelligence supercomputers in collaboration with Anthropic, an OpenAI rival that is working to push the frontier of what is possible with artificial intelligence. When completed, it will be five times larger than the cluster used to build Anthropic's current most powerful model. Amazon says it expects the supercomputer, which will feature hundreds of thousands of Amazon's latest AI training chip, Trainium 2, to be the largest reported AI machine in the world when finished. Matt Garman, the CEO of Amazon Web Services, revealed the supercomputer plans, dubbed project Rainer, at the company's Re:Invent conference in Las Vegas today, along with a host of other announcements cementing Amazon's rising dark-horse status in the world of generative AI. Garman also announced that Tranium 2 will be made generally available in so-called Trn2 UltraServer clusters specialized for training frontier AI.

Intel Corp. has decided to end development work on its Nervana neural network processors and will instead focus its efforts on the artificial intelligence chip architecture it acquired when it bought out Habana Labs Ltd. for $2 billion in December. The news was revealed Friday by Moor Insights & Strategy analyst Karl Freund in an article in Forbes. He said Intel told him it had decided to end its work on both the Nervana NNP-T training chips and the Nervana NNP-I inference chips, though it said it will still deliver on customer commitments for the latter. Habana has developed two AI chips of its own, namely the Habana Gaudi and the Habana Goya (pictured). The former is a highly specialized neural network training chip, while the latter is a processor used for the inference that uses neural networks in active deployments.

LONDON – Following the launch of its AI inference chip last year, Habana Labs (Tel-Aviv, Israel) has unveiled an AI training chip built on the same architecture that can outpace the incumbent technology by a substantial margin, and features on-chip RoCE (remote direct memory access over Converged Ethernet) communications for scalability. While the company's inference chip, Goya, set records for ResNet-50 inference back in September 2018, the new training chip, Gaudi, offers similar high performance. Gaudi can process 1650 images per second at a batch size of 64 when training a ResNet-50 network, which Habana claims is a new world record for this benchmark. This throughput is delivered at 140W power consumption, also a substantial advantage versus competing solutions, according to the company. Impressive, but is Habana's architecture designed specifically to beat the ResNet-50 benchmark, or will it offer similar throughput advantages for other types of neural networks?