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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.