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Tuesday, Amazon Web Services (AWS) revealed its plans to build an “Ultracluster” AI supercomputer, a groundbreaking project that will feature hundreds of thousands of AWS’s homegrown Trainium chips. This massive chip cluster, called Project Rainier, will be used by AI startup Anthropic, a company in which Amazon recently invested $4 billion. Set to launch in 2025, it aims to be one of the largest AI training supercomputers in the world. AWS also introduced a new server called Ultraserver, which consists of 64 interconnected chips designed for AI workloads. Additionally, AWS announced Apple as a new customer for its Trainium chips, further signaling its commitment to competing with Nvidia’s GPUs in the AI chip market.
The market for AI semiconductors is projected to grow significantly, from an estimated $117.5 billion in 2024 to $193.3 billion by 2027, according to International Data Corp. (IDC). Currently, Nvidia dominates the AI chip market, commanding around 95% of the space, as reported by IDC in December.
Matt Garman, CEO of Amazon Web Services (AWS), pointed out that Nvidia is currently the only major player in the GPU market for AI, and AWS aims to provide customers with more options. Amazon’s strategy involves updating its custom-designed chips, like the Trainium, to not only reduce the cost of AI services for customers but also to exert greater control over its supply chain. This move could decrease AWS’s dependency on Nvidia, a critical partner in supplying GPUs for cloud-based AI services, while offering an alternative solution to customers.
Google's new quantum chip, Willow, was unveiled in late 2024 and is a significant advancement in quantum computing. It's part of Google's ongoing efforts to develop practical quantum computers that can perform tasks beyond the capability of classical supercomputers. One of the most significant features of Willow is its ability to scale. It can handle a higher number of qubits compared to previous processors, with Google aiming to eventually build quantum computers with thousands or even millions of qubits.
This scalability is crucial for moving from basic quantum experiments to solving real-world problems in fields like drug discovery, materials science, cryptography, and machine learning.