Business and IT leaders are evaluating the use of AI technologies and applications to further innovation and operational efficiency today more than ever. The journey is far from simple however, so it’s worthwhile to understand how your adoption of AI can be simplified and accelerated with an efficient data storage platform, optimized for AI.
The Pure Data Storage Platform for AI is an effective solution for AI workloads for several reasons:
- High-Performance Storage: Pure Storage provides high-performance storage optimized for AI and machine learning workloads. Its architecture is designed to accelerate model training and inference across your data pipeline.
- Scalability: Scale your AI storage infrastructure by adding storage capacity and performance in granular increments non-disruptively. Since AI workloads often involve processing large data sets and performing complex calculations, non-disruptive scalability is essential to meet growing data and processing requirements.
- Data Accessibility: Pure Storage provides fast and reliable access to data, which is critical for AI applications. As AI models become increasingly complex and data-intensive, fast access to data is essential for training and inference tasks.
- Parallel Processing with GPUs: NVIDIA GPUs are often used in AI and deep learning applications due to their parallel processing capabilities. Pure Storage works efficiently with NVIDIA GPUs, keeping them fed with data to accelerate the training of AI models and inference tasks. The combination enables seamless data exchange between memory and GPU resources, reducing latency and improving overall performance.
- Data Management andIintegration of AI Workflows: FlashBlade's data management capabilities and API integrations facilitate seamless integration into AI workflows and pipelines. This allows data scientists and AI engineers to efficiently manage and analyze data without worrying about the complexity of storage infrastructure.
- Reliability and Data Protection: Pure Storage solutions are designed for reliability and data protection, ensuring that critical AI datasets and models are stored securely and can be recovered in the event of failure or data corruption.
