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GPUs enable massive parallelism where each core is focused on making efficient calculations to substantially reduce infrastructure costs and provide superior performance for end-to-end data science workflows.

If you would like to learn more about AI Then this white-paper is for you:

• While it’s easy to think about throughput as ”the”metric you need to focus on to optimize your GPU usage, the throughput does not accurately reflect the full nature of AI workloads. To optimize your data pipeline you need to worry about more than feeding massive amounts of data to your GPUs – IOPs and metadata are important as well
• Many AI deep learning workloads involve a significant amount of small files. Everything from millions of small images to IoT per-device logs for analysis, and more. Once pulled into the data pipeline, ETL-types of work normalize the data and then Stochastic Gradient Descent is used to train the model.
• Artificial intelligence workloads have requirements for performance, availability, and flexibility that are not well met by increasingly traditional storage platforms.
• As AI datasets continue to grow, the time spent loading data begins to impact workload performance.

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