Two major tech companies are teaming up to revolutionize AI infrastructure. AMD and OpenAI have announced a large-scale partnership that will see the deployment of thousands of GPUs to power advanced AI models. This collaboration aims to significantly boost AI computing capacity and accelerate innovations in the field. A Game-Changing Scale of AI Hardware Deployment
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All posts tagged in GPU Computing
OpenAI is planning to build five new huge data centers in the US, part of a major expansion with its partners Oracle and SoftBank. This effort will bring nearly 7 gigawatts of power capacity and involve over $400 billion in investments over the next three years. The goal? To keep up with the huge demand
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Many businesses excited about AI are finding out a hard truth: memory limits are holding back their progress. Everyone talks about powerful GPUs and big cloud servers, but the real bottleneck might be the memory bandwidth. That’s the speed at which data moves between the processing units and storage. If this doesn’t keep up, even
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Nvidia has announced a new AI chip called Rubin CPX that aims to change how we handle large-scale video and software tasks. This chip is designed to process huge amounts of data quickly, making it possible to generate long videos or complex code more efficiently. Nvidia expects to launch Rubin CPX by late 2026, and
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As AI systems grow more powerful, their performance is increasingly held back by bottlenecks in memory and networking. These limitations reduce GPU utilization and overall efficiency, preventing infrastructure from reaching its full potential despite heavy investments. The core issue comes down to the trade-offs in the communication technologies used inside data centers, especially for connecting
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Kubernetes has long been the go-to platform for running cloud applications and microservices. It’s powerful and flexible, with a big community behind it. But as generative AI becomes more popular, Kubernetes faces new challenges. Large language models, special hardware like GPUs and TPUs, and heavy request loads mean the platform needs to be more than
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