Summary
Pure Storage GenAI Pod is a powerful new solution that accelerates the deployment of data pipeline models and tools.
Like many others, you’re likely grappling with the challenge of creating an AI solution that drives meaningful productivity gains across your organization. Industry analysts agree—delivering tangible results with AI is no longer optional; it’s a mainstream necessity. According to analyst firm ESG’s “State of the Generative AI Market” report from September 2024, over 75% of enterprises are planning for, in pilot, or in production use of GenAI. What’s truly remarkable is the expansive impact AI is making across functions such as R&D, engineering, marketing, customer support, research, and operations just to name a few. The rapid evolution of AI, from personal use to enterprise-wide adoption, has been nothing short of extraordinary. The Stanford Human-Centered Artificial Intelligence State of AI Index highlights this shift, noting that “more Fortune 500 earnings calls mentioned AI than ever before, and new studies show that AI tangibly boosts worker productivity.“ This underscores the immense transformative potential of AI to revolutionize industries and redefine how businesses operate.
Pilots in the Cloud
So imagine you’re tasked with developing a new GenAI application to optimize marketing or customer support team productivity. While it is not always obvious where to start, most begin with OpenAI or Google’s Gemini, as initial flexibility and their APIs allow you to forget about infrastructure and experiment to see what is possible. As you expand your usage and need to utilize your organization’s sensitive and proprietary data, public cloud models become more concerning, as data control and security (think data exposure, unauthorized access, model manipulation, and improper API key management) become critical and governance issues are still undefined.
The combination of economics at scale and greater control over the use of sensitive data are significant factors that compel AI initiative leaders to bring these projects on premises.
On-premises Deployment Benefits
Most don’t realize that there are significant benefits to deploying in your own cloud giving you the flexibility in cost, performance, and enterprise features for security and reliability for serious production scale. Once GenAI projects are ready to move beyond experimentation to global production engineering, on-premises deployments allow these enterprises to leverage their proprietary data while maintaining any control and governance requirements necessary for their holistic operation.
On-premises Deployment Challenges
As you take the first steps to build your own cloud for GenAI pipeline deployment, there are a few best practices to consider to avoid future bumps in the road that can be time-consuming and demand a high level of expertise to support.
Common problems associated with deployment of GenAI pipelines include:
- Right-sizing the architecture for optimum performance across storage, compute, and network depending on use case, performance requirements, and size of your data, especially when supporting both inference and training phases
- Reducing the friction of installing and deploying the hardware, software, and tool stacks required to achieve specific use case goals
- Deploying and cataloging curated, GPU-optimized, pre-trained, domain-specific, and foundation models and accompanying data sets
- Monitoring the AI/ML deployments and scaling the clusters accordingly to accommodate the incoming user demand
- Providing expertise and skills to deploy, manage, and operate a robust AI data pipeline
The consequence of not investing in the appropriate supporting infrastructure and platform stacks for GenAI (and AI more generally) could amount to your organization not supporting the very efforts that keep you from becoming irrelevant in the GenAI boom.
Pure Storage GenAI Pods Accelerate Your AI Vision into Reality
At Pure Storage, we help customers simplify and accelerate AI to achieve their goals faster, with better operational efficiency, and with future AI growth in mind. Pure Storage GenAI Pod provides a powerful turnkey solution that integrates all of the necessary components to facilitate the simple on-prem deployment of AI/ML systems. This includes the hardware, cluster management software, MLOps platform, and the pre-trained foundational models that power real-world GenAI deployments, including modern retrieval-augmented generation (RAG) pipelines that go beyond the language modality (video surveillance, for example).
Pure Storage GenAI Pods provide pre-defined stack configurations and sizes based on different use case parameters, including things like size of the model, number of parallel users, etc. They’re optimized, making it easy, performant, and cost-effective for your organization to stand up the right configuration for your needs.
Figure 1. Pure Storage GenAI Pod Example Stack
Automating Deployment of NVIDIA NIM and Vector Databases
NVIDIA NIM offers a major step forward in reducing time to deployment of foundation models, but we wanted to take it a step further with Portworx® by Pure Storage. Portworx in GenAI Pods provides a leading, enterprise Kubernetes data management platform, enabling as-a-service delivery and one-click deployments to accelerate AI application development, including NVIDIA NIM as well as vector databases such as Milvus and pgvector. However, everybody knows it’s not just about getting deployments off the ground—with Kubernetes and Portworx, we’ve also streamlined and automated lifecycle management and operations, so your teams can stay focused on driving AI innovation, not on managing dependencies and infrastructure.
Best-of-breed GenAI Ecosystem
Pure Storage GenAI Pod provides the MLOps capabilities that production-level GenAI pipeline deployments require so your team can fast-track AI deployments and monitoring—whether they’re working with cutting-edge vector databases, foundation models, or more traditional AI/ML pipelines such as supervised learning applications. We have joined forces with a best-of-breed ecosystem of tech leaders, including Arista, Cisco, KX, NVIDIA, and others, to create the GenAI Pod, a complete solution that includes everything organizations need—from the hardware and software platform to pre-trained AI models and vector databases.
Real-world Applications
With GenAI Pod as the base technology, companies can build off of this foundation to deliver AI outcomes right away in several key areas. The first vertical solutions include drug discovery powered by NVIDIA BioNeMo and trade research, execution, and risk management with KX. The first horizontal solution is RAG with a wide variety of use cases, including enterprise search, multimodal PDF extraction and chatbots for intelligent document processing systems, automated code review/generation, and cutting-edge agentic workflows.
We see the GenAI Pod solution as a foundational technology that enterprises can further expand upon as new AI/ML advances emerge with an optimized infrastructure bedrock sufficient for full-on production MLOps deployment, not just R&D experimentation.
The addition of GenAI Pod solutions to the Pure Storage AI portfolio complements our suite of AI architectures. AIRI® powered by NVIDIA DGX BasePOD, FlashStack® for AI, and FlashBlade//S™ with NVIDIA DGX SuperPOD provide innovative foundations for customers getting started or bolstering their AI infrastructure today. Whether you’re beginning your AI journey or building an AI supercluster, the Pure Storage platform for AI is there with you every step of the way.
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