The U.S. AI market size accounted for $103.7 billion in 2022 and is estimated to reach around $594 billion by 2032, according to Precedence Research. Sure, the AI market is booming. However, businesses are at a crossroads where the integration of foundation and traditional AI models becomes paramount for sustained success.
The key to unlocking the economic potential of these models lies in a strategic emphasis on data management. Companies need to thoroughly assess their current data capacity and project future growth to ensure that their infrastructure can handle the increasing demands of advanced AI algorithms.
Equally critical is the evaluation of the geographical distribution and security measures surrounding the data. Businesses also must consider how data is accessed and shared internally, and they must strategically plan and invest in future data storage solutions that align with their AI objectives.
That sounds like a lot, but it does not need to be with the help of IBM. IBM recently introduced the new IBM Storage Scale System 6000, a cloud-scale global data platform designed to meet today's data intensive and AI workload demands, and the latest offering in the IBM Storage for Data and AI portfolio.
The IBM Storage Scale System 6000 is designed for efficient storage of daily-generated semi-structured and unstructured data, encompassing video, imagery, text and instrumentation data. It enhances an organization's digital footprint across hybrid environments.
The incorporation of IBM FlashCore Modules in the first half of 2024 promises greater data efficiencies and economies of scale. The new maximum capacity NVMe FCM is expected to provide cost and energy savings, boasting 70% lower cost and 53% less energy per terabyte compared to IBM's previous maximum capacity flash drives for the Storage Scale System. This not only optimizes NVMe performance but also leverages the cost advantages of Quad-level Cell.
The Storage Scale System 6000 with FCM offers powerful inline hardware-accelerated data compression and encryption, ensuring data security in multi-user, multi-tenant environments. It supports 2.5 times the amount of data in the same floor space compared to the previous generation system.
To accelerate the adoption and operationalization of AI workloads, the system is equipped with IBM watsonx, featuring a new NVMeoF turbo tier, parallel multi-tenant data isolation and IBM-patented computational storage drives for enhanced performance, security and efficiency in AI workloads.
The Storage Scale software, serving as the global data platform for unstructured data, connects with a multi-vendor storage ecosystem that includes AWS, Azure, IBM Cloud and other public clouds, along with IBM Storage Tape.
The Storage Scale System 6000 also connects NVIDIA AI solutions to diverse workloads, utilizing IBM's NVMeoF turbo tier for efficient handling of small files and transactions. It supports NVIDIA Magnum IOTM GPUDirect Storage for improved performance in data movement IO, and with NVIDIA ConnectX-7 NICs, it enables high-speed connectivity, enhancing performance between nodes and to NVIDIA GPUs.
Additionally, users can experience faster access to data with over 2.5 times the gigabytes per second throughput and twice the input/output operations per second performance compared to competitors. This enables high-processing throughput and access speed for multiple concurrent AI and data-intensive workloads, catering to a range of use cases.
One of those use cases happened at the University of Queensland in Australia. The research institution accelerated a wide range of workloads providing faster access to data and improved efficiency and capabilities using the IBM Storage Scale global data platform and IBM Storage Scale System. Examples of some of the research where IBM Storage is used include applied AI for the characterization of neurodegenerative diseases and in the search for more effective and flexible vaccine technologies.
"The potential of today's new era of AI can only be fully realized, in my opinion, if organizations have a strategy to unify data from multiple sources in near real-time without creating numerous copies of data and going through constant iterations of data ingest," said Denis Kennelly, general manager, IBM Storage. "IBM Storage Scale System 6000 gives clients the ability to do just that – brings together data from core, edge and cloud into a single platform with optimized performance for GPU workloads."
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