Powering generative AI solutions with unified data storage

While GenAI adoption has grown exponentially, there's still a critical element that needs increased attention in enterprise AI discussions: building the right infrastructure. Without that, it's difficult to scale or even deploy AI use cases that could transform your business. 

One key infrastructure challenge that gets in the way of faster AI growth is that GenAI applications often require significant data resources. For example, the size and complexity of datasets used by RAG workloads require scalable operations, efficient real-time processing, and strong data strategies that lay the foundation for effective performance today and expanded growth tomorrow.  

Many organizations are struggling due to infrastructure limitations. Research from the AI Infrastructure Council cites challenges such as data management, latency, computing capabilities, and power as critical factors holding back AI innovation. IDC found that over 63% of businesses say their storage infrastructure needs major improvement or a complete overhaul to meet the needs of AI. 

Do you have the right infrastructure in place to get the most from the GenAI revolution? Let’s find out. 

AI’s rapid growth stems from advancements in areas like retrieval-augmented generation (RAG) workloads and large language models (LLMs). By using the data stored in LLMs, RAG workloads identify the most relevant source material and produce outputs tailored to the query—whether designing an image, writing an email, or identifying a treatment for a previously untreatable disease. 

Today, GenAI users have access to capabilities like text generation, text-to-image synthesis (such as with DALL-E), and multimodality that integrates text, video, and sound as both inputs and outputs. These new innovations and tools are supported by the rapid scaling capabilities of LLMs, which rely on extensive and often proprietary datasets for training, fine-tuning, and real-time inferencing. 

Investing in the right infrastructure—especially data storage—for AI is crucial. Unified data storage eliminates data silos and delivers flexibility, speed, and performance to AI workloads. With unified data storage solutions for GenAI you can: 

• Speed and streamline data management. LLMs need robust, scalable storage to handle exponential data requirements during training, fine-tuning, and deployment. Consider how a RAG workload functions: a vast dataset is at the ready. When a user enters a prompt or query, the solution finds relevant data and uses it to generate a targeted, coherent response. Unified data storage ensures fast, efficient access to these datasets, so unnecessary delays don’t slow down performance. 
• Secure data across environments. Data silos can make it difficult for AI applications to efficiently access the information they need. Seamless data migration and protection are essential in GenAI environments. NetApp AI solutions simplify moving datasets across hybrid or multicloud ecosystems—while safeguarding data with built-in, proactive security solutions. 
• Cost-effectively achieve performance goals. If budget wasn’t a concern, performance wouldn’t be an issue. Yet the reality for organizations powering RAG workloads is that fast data access must be balanced with cost. For example, research from the AI Infrastructure Council found that, while 96% of leaders plan to invest in their AI infrastructure, the top concern for cloud and hybrid investments was preventing waste or idle costs. NetApp AI solutions give you the scalable storage to drive performance and the intelligent resource allocation to keep costs in check. 

Think of investing in infrastructure as the best way to help deliver long-term ROI on AI. Investing in AI infrastructure isn’t just relevant to today’s performance. It’s also the foundation for successful future investments. Your data strategy is a secret weapon that enables you to adapt to whatever’s next. 

Leaders should consider how they can simplify operations to focus on AI-driven innovation, instead of difficult infrastructure management. Streamlined data management will help you reduce operational complexity. This lets you put the focus on innovation rather than constantly struggling to solve infrastructure challenges. Better integrations and streamlined data storage management can help give organizations the confidence to make AI investments, knowing a positive ROI is more likely. 

AI tools also need secure environments. Tools that support strong security and data governance strategies help businesses meet regulatory requirements while protecting sensitive information. With evolving cyberthreats, it’s important that complex workflows validate access and queries at every stage. Unified, scalable storage balances performance, cost, and flexibility both now and in the future. 

Why are customers choosing NetApp AI solutions to help drive their most important AI innovations? With NetApp, they can 

• Innovate with unified data storage. NetApp delivers comprehensive support for data mobility, governance, and efficiency across on-premises, hybrid, and multicloud environments. NetApp AIPod offers high-performance training and inferencing to improve GenAI and RAG workloads. 
• Get to market faster with seamless integration. Don’t waste time struggling with interoperability challenges. NetApp integration capabilities enable you to optimize your AI ecosystems. With seamless integrations, it’s easier to complete AI-driven product deployments and capture a time-based competitive advantage. 
• Get high-performance without high costs. Low-performance, high-latency environments can quickly destroy the value from AI applications. NetApp offers the visibility and control needed for smart, cost-effective provisioning while meeting performance needs. NetApp ONTAP provides cost-effective solutions and works with some of today’s most important solutions like NVIDIA GPU Direct Storage and DGX BasePOD. It’s flexible and simple to deploy and streamline operations, which drives cost-effectiveness. 
• Secure data protection. AI relies on data, and it’s critical to keep your organization’s most important asset protected. NetApp AI solutions have built-in capabilities to support security at every level, like autonomous ransomware protection, SnapLock® for immutable storage, and Zero Trust security frameworks. Protect your sensitive GenAI datasets and maintain trust in AI-powered operations. 

GenAI applications, including LLMs, RAG workloads and emerging innovations, drive critical enterprise growth, efficiency, and experimentation. Investing in the right infrastructure and data strategy lays the foundation to scale and deploy AI use cases across the enterprise. With NetApp, you can build a data strategy to meet the challenges of scaling AI innovation while exploring the constantly expanding AI horizon. Start building an intelligent data infrastructure for tomorrow’s AI expansion today. Learn more about NetApp’s GenAI offerings and how they can help you today.