{"id":33385,"date":"2023-09-14T03:00:00","date_gmt":"2023-09-14T10:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=33385"},"modified":"2023-09-13T11:07:08","modified_gmt":"2023-09-13T18:07:08","slug":"ddn-storage-solutions-deliver-700-gains-in-ai-and-machine-learning-for-image-segmentation-and-natural-language-processing","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/09\/14\/ddn-storage-solutions-deliver-700-gains-in-ai-and-machine-learning-for-image-segmentation-and-natural-language-processing\/","title":{"rendered":"DDN Storage Solutions Deliver 700% Gains in AI and Machine Learning for Image Segmentation and Natural Language Processing"},"content":{"rendered":"
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\"\"<\/figure><\/div>\n\n\n

DDN<\/a>\u00ae, a leader in artificial intelligence (AI) and multi-cloud data management solutions, announced impressive performance results of its AI storage platform for the inaugural AI storage benchmarks released this week by MLCommons Association. The MLPerfTM<\/sup>\u00a0Storage v0.5 benchmark results confirm DDN storage solutions as the gold standard for AI and machine learning applications.<\/p>\n\n\n\n

DDN’s entries cover Image Segmentation and Natural Language Processing categories of the MLPerf Storage Benchmark. Using powerful single and multi-node GPU configurations, DDN\u2019s <\/a>A<\/a>3<\/sup><\/a>I<\/a>\u00ae<\/a> AI400X2 storage appliance effortlessly scales to deliver faster and more reliable data access, while maximizing GPU utilization and delivering highest efficiency for demanding AI workloads.<\/p>\n\n\n\n

In an individual compute node evaluation, a single DDN AI400X2 NVMe appliance equipped with DDN\u2019s EXAScaler\u00ae<\/a> 6.2 parallel filesystem fully served 40 AI accelerators, delivering a remarkable throughput of 16.2 GB\/s.1<\/sup> In a multi-node configuration, the same DDN AI400X2 NVMe appliance quadrupled its output, serving 160 accelerators over ten GPU compute nodes, achieving a throughput of 61.6 GB\/s.2<\/sup> These remarkable results demonstrate 700% better efficiency on a per storage node basis when compared to the competitive on-premises solution submissions.<\/p>\n\n\n\n

\u201cDDN’s cutting-edge data storage solutions fuel and accelerate GPUs in data centers and in the cloud, helping organizations develop better cancer detection methodologies, putting safe and reliable robotaxis on our roads and highways, and bringing to market more effective chatbots and virtual assistants to make our lives easier,\u201d said Dr. James Coomer, SVP of Products at DDN. \u201cWe’re proud to lead the way in safe and power-efficient AI adoption, setting new standards for innovation and performance in the industry.\u201d<\/em><\/p>\n\n\n\n

The ability to power AI workloads, machine learning and Large Language Models at highest levels of efficiency and scale, while minimizing power usage and data center footprint is critical. With thousands of systems deployed on premise and in the cloud, DDN\u2019s AI infrastructure storage systems are the solution of choice to power GPUs for the most demanding and innovative organizations in the world.<\/p>\n\n\n\n

About MLCommons<\/strong><\/p>\n\n\n\n

MLCommons is an open engineering consortium with a mission to make machine learning better for everyone through benchmarks and data. The foundation for MLCommons began with the MLPerf benchmark in 2018, which rapidly scaled as a set of industry metrics to measure machine learning performance and promote transparency of machine learning techniques. In collaboration with its 50+ members – global technology providers, academics, and researchers, MLCommons is focused on collaborative engineering work that builds tools for the entire machine learning industry through benchmarks and metrics, public datasets, and best practices.<\/p>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

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Join us on Facebook: https:\/\/www.facebook.com\/insideBIGDATANOW<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

DDN\u00ae, a leader in artificial intelligence (AI) and multi-cloud data management solutions, announced impressive performance results of its AI storage platform for the inaugural AI storage benchmarks released this week by MLCommons Association. The MLPerfTM\u00a0Storage v0.5 benchmark results confirm DDN storage solutions as the gold standard for AI and machine learning applications.<\/p>\n","protected":false},"author":10513,"featured_media":33238,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,62,66,68,180,268,56,1],"tags":[1216,117,941,364,96],"acf":[],"yoast_head":"\nDDN Storage Solutions Deliver 700% Gains in AI and Machine Learning for Image Segmentation and Natural Language Processing - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2023\/09\/14\/ddn-storage-solutions-deliver-700-gains-in-ai-and-machine-learning-for-image-segmentation-and-natural-language-processing\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DDN Storage Solutions Deliver 700% Gains in AI and Machine Learning for Image Segmentation and Natural Language Processing - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"DDN\u00ae, a leader in artificial intelligence (AI) and multi-cloud data management solutions, announced impressive performance results of its AI storage platform for the inaugural AI storage benchmarks released this week by MLCommons Association. 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DDN announced the next generation of SFA\u00ae platforms, new data management features of its EXAScaler\u00ae \u201cEXA5\u201d file solution, which will be generally available over the next two calendar quarters.","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":22340,"url":"https:\/\/insidebigdata.com\/2019\/03\/21\/transformative-solutions-for-accelerating-ai-analytics-and-deep-learning-at-nvidia-gtc19\/","url_meta":{"origin":33385,"position":4},"title":"Transformative Solutions for Accelerating AI, Analytics and Deep Learning at NVIDIA #GTC19","date":"March 21, 2019","format":false,"excerpt":"One pivotal message received by attendees of this week's NVIDIA GPU Technology Conference (GTC) in Silicon Valley is the importance of game-changing storage solutions and applications that empower users to accomplish their most challenging AI objectives.","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":23321,"url":"https:\/\/insidebigdata.com\/2019\/09\/24\/insidebigdata-guide-to-optimized-storage-for-ai-and-deep-learning-workloads-part-2\/","url_meta":{"origin":33385,"position":5},"title":"insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads – Part 2","date":"September 24, 2019","format":false,"excerpt":"Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. 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