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AI/HPC Convergence

Storage that fuels emerging innovations

Accelerate Training and Inference Workloads

Whether it comes to autonomous driving, speeding up reporting of cancer data, or accelerating data science discovery, enterprises are adopting AI, ML, and DL technologies to process, analyze and act on rapid influxes of new large data sets. These complex and rapidly expanding AI use cases require ingestion of vast amounts of data to train the algorithms, which drives the demand for storage infrastructure that is powerful, flexible and highly scalable.


Foundation for Future Use Cases

With Panasas next-generation ActiveStor solutions, you can accelerate time to decision by taking advantage of the PanFS parallel file system running across multiple nodes, eliminating bottle necks for large file transfers and ensuring consistent performance regardless of scale. You can put both training and inference capabilities on the same platform while ensuring a uniform view of data across all phases of the AI process such as ingestion, preparation and learning. In addition, Panasas hybrid storage arrays are designed to efficiently store data across flash and spinning disks to minimize cost while still delivering extreme performance.

Artificial Intelligence (Deep Learning)

Most complex AI solutions make use of sophisticated neural networks that require and use the massive computing power of GPUs. This in turn demands more intelligence in the storage infrastructure that must support this never-ending need for increasing functionality and performance.

Machine Learning

When it comes to the statistical approaches to AI, accuracy and time-to-model matter, and the size of the data sets keeps growing. Training scales in a near-linear fashion which requires an optimized and linearly scalable storage infrastructure.