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FlexPod for AI: the gold standard for ML workloads, now with GenAI

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Bruno Messina
Bruno Messina

As organizations incorporate AI into their day-to-day applications and processes, they need to do it in an operationally simple manner. Leveraging the best of NetApp and Cisco, FlexPod® shared infrastructure solution has been operationalizing and simplifying enterprise and modern workload deployments for thousands of customers for over a decade. That’s why we’re offering NetApp customers the pinnacle of artificial intelligence and machine learning testing, integration, reliability, time-to-value, and scalability with updates to FlexPod for AI, including a new reference architecture for Generative AI (GenAI).

FlexPod for AI stands as the gold standard in workload testing and offers unique capabilities to unlock the true potential of your fine-tuning and inferencing AI applications. FlexPod for AI is an easy decision for any IT director, server or application administrator, or VP of applications, especially with the following five key benefits.

Flexpod Logo

Unmatched time to value

FlexPod for AI reference architectures save customers time, increase productivity, and decrease cost because all parts and steps of the solution are documented. FlexPod for AI reference architectures are pre-tested against AI models, choosing the correct hardware and NVIDIA GPU accelerators, identifying bottlenecks, and optimizing resource allocation. Across all FlexPod architectures, customers have seen 20%-time savings in network management and maintenance, and close to $2.5 million average per customer in cost savings from reduced risk, increased flexibility, and enhanced efficiency. FlexPod for AI customers should see similar benefits. 

FLexpod DIagram

Three new FlexPod reference architectures underscore this rapid time to value.

FlexPod with Generative AI inferencing is an A-to-Z reference architecture that is a blueprint to run ML models from Hugging Face, NVAIE and GitHub on your FlexPod running Red Hat OpenShift inside of vSphere virtual machines. Prospective customers gain:

  • Time savings and error reduction with deployment ready Ansible playbooks for the base FlexPod setup and for future FlexPod AI additions.
  • Support for component monitoring, solution automation / orchestration, and workload optimization.
  • A highly available and scalable platform that supports various deployment ML models from NVMAIE, Hugging Face and GitHub.
  • Lower costs, better scalability, less risk and faster time to value.
  • A solution optimized infrastructure for sustainability and upgradeability.

FlexPod Datacenter with SUSE Rancher for AI Workloads Design Guide combines SUSE Rancher ECM with FlexPod to simplify the deployment and management of the container infrastructure. Ansible integration with the FlexPod solution automates the deployment of the FlexPod infrastructure along with SUSE Rancher installation. This integration enables customers to take advantage of programming and automates the infrastructure at scale with agility, extending the benefits of automation to the entire stack.

Scaling FlexPod for GPU intensive applications summarizes SpecHPC 2021-based benchmark applications targeted for real-life model simulation. This FlexPod solution demonstrates:

  • Simplified deployment and operation of general-purpose AI workloads.
  • Seamless integration into AI ecosystems.
  • Operational simplicity and efficiency.
  • Accelerated time to value and faster AI implementation.
  • Integrated full-stack security.
  • Near-linear scalability, demonstrated through benchmark tests, showcasing consistent performance even with varying dataset sizes.

Seamless integration

FlexPod with the NVIDIA AI Enterprise stack and the NetApp® AI control plane delivers full-stack innovation across accelerated infrastructure, enterprise-grade software, and AI models. FlexPod with NIVIDA AI Enterprise accelerates the entire AI workflow. AI/ML projects reach production faster, with higher accuracy, efficiency, and infrastructure performance at a lower overall cost. The NetApp AI control plane provides comprehensive data management of AI/ML data and experiments by making physical storage efficient and using Kubeflow to simplify the deployment of AI workflows.

Unparalleled reliability

FlexPod for AI helps to ensure the reliability of your AI workloads. It provides a resilient infrastructure that can handle the most demanding AI applications without compromising performance or data integrity. All paths and components are redundant, leaving no single point of failure. FlexPod for AI servers can be replaced or scaled in minutes with no extra wiring, not days or weeks like competing solutions.

Robust security

FlexPod for AI provides a robust and reliable security framework. Trust is established and enforced through device hardening, micro-segmentation, least-privilege access, and an end-to-end secure value chain, which means that your data and applications are shielded from threats. In the unlikely event of a breach, the FlexPod policy-based server profiles and data recovery capabilities act as a "golden shield," swiftly restoring servers, applications, and data to their secure state, so that you can resume operations with confidence.

Cisco Webpage

Unmatched scalability

FlexPod for AI offers unmatched scalability. It can seamlessly scale up or out to accommodate growing AI workloads, helping organizations to expand their AI initiatives. You add servers and storage through software, easily adding spares and new capacity in minutes, saving time and money, while decreasing errors.

FlexPod for AI represents the gold standard of workload testing for AI applications. Its reliability, performance, and scalability make it the foundation on which your organization can build its AI-driven future.

Find out more

Learn more about how FlexPod and its reference architecture portfolio can enhance your AI/ML workload:

Bruno Messina

Bruno Messina joined NetApp in 2018 and works in product marketing for FlexPod. His previous experience includes a career in product marketing of UCS servers for Cisco Systems and Solaris server marketing and competitive analysis at both Oracle and Sun Microsystems, where he joined in 2000. Bruno spent ten years in various roles of competitive analysis and product management at Sun Microsystems, leading analysis in both the workgroup and enterprise servers. Prior to Sun Microsystems, Bruno spent time finishing his MBA education and worked for two years at Cadence working on product marketing for both board-level and board timing tools. Bruno holds both a BSEE and MBA from Rensselaer Polytechnic Institute in Troy, N.Y.

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