Digital transformation is sweeping through every industry and vertical.
Engineers are designing artificial intelligence (AI)-powered data platforms that transform businesses, show insight into customer behavior, and help leaders plan for the future.
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As a solution provider, you may find that your clients still have questions and face challenges in implementing their own data strategies.Â
Here’s what your end-customers need from their AI-powered data management platforms*
Reduce time in importing and preparing data
Keep only a single copy of the data
Eliminate infrastructure siloes
Foster global accessibility and collaboration
Securely manage, protect and govern data
Promote sustainability
Arrow, IBM and NVIDIA | Better together
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IBM has the capabilities to complete the AI infrastructure puzzle with data management. It starts with extreme performance but expands to the workflow level for scalable ingest, tagging and workloads that run from edge to core to cloud. This holistic view includes multi-protocol storage support, data orchestration, maintaining only a single copy of the data, backup, archive, data recovery and data life cycle management for storage economics, all underpinned with data security and cyber resiliency.
 
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NVIDIA has a solution-oriented approach to make AI easy to use and adopt. Their GPUs consistently have the highest-performing compute capabilities available, often making it challenging to feed data quickly enough. To overcome this obstacle, NVIDIA relies on partners like IBM to help provide high-performance storage. NVIDIA offers the toolset data scientists and analysts need and has a GPU direct-to-storage (GDS) capability to improve data performance with GPU memory. NVIDIA also acquired Mellanox in 2020 to add high-end networking capability.
That’s why Arrow teamed up with IBM and NVIDIA.
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IBM and NVIDIA are leaders in helping customers with AI, analytics and high-performance computing (HPC). The go-to-market relationship between IBM Storage and NVIDIA started developing in 2016 after NVIDIA launched DGX servers and solidified during the public release of joint reference architectures in 2019. Â
IBM Storage Scale with NVIDIA DGX
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IBM has worked with NVIDIA to create a solution for IBM Storage Scale for AI with NVIDIA DGX servers.Â
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This is a scalable, software-defined infrastructure powered by IBM Storage Scale and NVIDIA DGX systems for machine learning (ML)/deep learning (DL) workloads. The solution is certified with a reference architecture and benchmarks, plus many other seller and client assets.Â
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The IBM Storage Scale and NVIDIA DGX offering is fulfilled by a business partners who have the required NVIDIA and IBM Elastic Storage Server and IBM Storage Scale certifications.
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In this solution, the ESS 3500 NVMe storage system has demonstrated the ability to drive 91 GB/s per system in a small, 2u form factor. This composable reference architecture can grow from 1 DGX BasePOD server up to 9 DGX servers per rack, which is 72 NVIDIA GPUs.
Partner with Arrow, IBM and NVIDIA
Skilled partners like you are a key component in helping customers implement and run these systems for customers. You can simply pull together a whole solution and ensure client success, while your clients still have access to IBM and NVIDIA support.
IBM and NVIDIA resources
- IBM Storage for AI with NVIDIA DGX | Quick Reference GuideÂ
- NVIDIA DGX total cost of ownership (TCO) tool | Presentation
- IBM Spectrum Storage for AI with NVIDIA DGX | Systems Solution BriefÂ
- Scaling AI with IBM Storage and NVIDIA DGX Seller | PresentationÂ
Technical resources
- NVIDIA DGX SuperPOD: IBM Spectrum Scale and ESS 3200 | Reference architectureÂ
IBM sales resources:
IBM technical pre-sales resources
- Data and AI storage technical recordings
- IBM TechZone PortalÂ
- Technical sales intermediate trainingÂ
- Level 3 for technical sales training
Customer resources
