The End of Data Migration: How GPU-Accelerated Storage is Revolutionizing Enterprise AI
NVIDIA's AI Data Platform brings GPU acceleration directly into storage systems, enabling continuous, secure data preparation without the traditional risks and inefficiencies of data migration for enterprise AI.
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Why it matters
This approach could eliminate a major bottleneck in enterprise AI adoption by solving the data preparation challenge in a secure and efficient manner.
Key Points
- 1The traditional approach of copying and moving enterprise data to make it
- 2 creates security vulnerabilities, increases costs, and wastes up to 80% of data scientists' time on data wrangling
- 3NVIDIA's AI Data Platform embeds GPU acceleration into traditional storage systems, enabling continuous, in-place data transformation without the security risks and inefficiencies of data copying
- 4This approach addresses the critical challenge of
- 5 - the combined rate at which enterprises generate new data and modify existing data
- 6GPUs in storage can continuously prepare data for AI as a background process, freeing data scientists from time-consuming data preparation tasks
- 7Storage vendors are embracing this paradigm shift, going beyond NVIDIA's reference designs to create innovative solutions
Details
The promise of AI agents transforming enterprise work has captured boardroom attention worldwide, but a fundamental infrastructure problem has been quietly sabotaging deployment efforts. The culprit isn't the AI models themselves—it's the data infrastructure beneath them. Making unstructured enterprise data
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