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Generative AI models have grown 10x in parameters per year for the last five years. That's why we developed AI Hypercomputer based on our decades of learning taking AI from development into production. AI Hypercomputer brings together performance-optimized hardware across compute, storage, and networking, open software with support for all of the widely adopted AI frameworks such as JAX and PyTorch, and flexible consumption models that are designed for AI workloads, such as Dynamic Workload Scheduler. AI Hypercomputer consists of a system of technologies optimized to work in concert to enable modern AI workloads while also offering flexibility by enabling choice at every layer for your unique use case.
Access your complimentary copy of the report to learn why Google was named a Leader.
Download our latest ebook on building an infrastructure optimized for AI workloads.
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Generative AI models have grown 10x in parameters per year for the last five years. That's why we developed AI Hypercomputer based on our decades of learning taking AI from development into production. AI hypercomputer brings together performance-optimized hardware across compute, storage, and networking, open software with support for all of the widely adopted AI frameworks such as JAX and PyTorch, and flexible consumption models that are designed for AI workloads, such as Dynamic Workload Scheduler. AI Hypercomputer consists of a system of technologies optimized to work in concert to enable modern AI workloads while also offering flexibility by enabling choice at every layer for your unique use case.
Access your complimentary copy of the report to learn why Google was named a Leader.
Download our latest ebook on rearchitecting your infrastructure for generative AI.
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Developer Advocate of Machine Learning,
Google Cloud
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Customer Engineer,
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Access your complimentary copy of the report to learn why Google was named a Leader.
Download our latest ebook on rearchitecting your infrastructure for generative AI.
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