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HPE introduces new storage options within the Greenlake for File Storage platform. These are intended to accelerate AI workloads but also make them more energy efficient at the
HPE introduces new storage options within the Greenlake for File Storage platform. These are intended to accelerate AI workloads but also make them more energy efficient at the same time.
HPE announces new improvements to GreenLake for file storage. These updates include the ability to support large AI and data lake workloads and enable high-density, 100 percent all-flash options. The improvements aim to deliver high performance, ease of use and efficiency in AI and help companies realize their artificial intelligence ambitions.
The new storage options offer four times the capacity and up to twice the system performance per rack unit compared to the current version of HPE GreenLake for File Storage. This leads to a tripling of data throughput, but also a reduction in power consumption by up to 50 percent. Advanced technology such as dual compute controllers and fully NVMe storage units enable customers to accelerate their workloads while reducing data center costs, power consumption and carbon footprint.
These improvements support data scientists, application administrators, and IT staff by improving productivity and providing tools to reduce power consumption. With the latest high-density storage racks, HPE GreenLake for File Storage increases its capacity density.
The adjustments are not made just like that. HPE wants to further optimize the storage component within GreenLake for a trend that everyone wants to hear about today: AI. GreenLake for File Storage should cover all phases of AI workflows, from data aggregation and preparation to training, optimization and inference. The solution delivers long-lasting, fast performance that is essential for the most demanding AI applications.
Additionally, HPE GreenLake for File Storage leverages advanced data reduction technologies to increase efficiency. This includes storage-friendly snapshots of business data, native replication, and improved data reduction. These technologies enable customers to achieve significant savings and accelerate AI workloads, resulting in faster model training and optimization.
Source: IT Daily
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