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GPUs Power Complex AI Workloads

GPUs are revolutionizing data processing, enabling parallel processing for AI models, neural networks, and research acceleration.

If you would like to learn more about GPU Then this white-paper is for you:

• The latest GPU from NVIDIA, Blackwell, boasts 30 TB of unified memory over a 130 TB/s compute fabric, creating an exaFLOP AI supercomputer. Scaling and optimizing GPU setup is crucial for GPU efficiency, as data stalls can hinder GPU performance.
• A modern data platform and storage solution designed for high-performance computing are necessary to optimize GPU investment.
• Traditional data pipeline architectures struggle to deliver data quickly enough to keep GPUs running continually.
• The performance of large HPC or AI systems heavily depends on the utilization rates of GPUs.
• Storage choices are critical to efficient running of these systems. Solid-state drives (SDDs) have changed the storage landscape, with SSD devices being the correct choice for most GPU environments. Parallel file systems have been used in HPC environments for some time, but traditionally they have been used to reduce bottlenecks.

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