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Organizations are harnessing AI for innovation, requiring large datasets and intense compute power. This brief examines supercomputing's role in speeding AI model training and results.

Covered topics:

· Need for high GPU performance for big AI workloads, including language and recommendation models
· Integrated software stack and lifecycle services benefits for system management
· CPU supercomputing limits, environmental impact, and cloud costs

Discover how to scale AI models swiftly with supercomputing by reading the full brief.

Vendor:

Hewlett Packard Enterprise

Posted:

Jul 9, 2024

Published:

Jul 9, 2024

Format:

HTML

Type:

Research Content