These days, it seems like everyone in enterprise IT is talking about GPUs, and thinking about how to get the GPU capacity (and associated power and cooling) they need. It’s easy to see why: GPUs are becoming more powerful and enabling advanced use cases that never would have been possible before now.
While GPUs have been groundbreaking for AI development, they don’t have to be the centerpiece of every enterprise AI strategy. Most organizations don’t need to train models from scratch; instead, they can leverage pretrained models from providers like Hugging Face and fine-tune them for their specific needs. This approach minimizes the need for large, expensive GPU clusters dedicated solely to training, though GPUs may still be required for fine-tuning and inference tasks.
The immediate focus should instead shift to extracting value from private datasets. Enterprises can achieve this by implementing inference (i.e., the process by which AI models generate predictions) that incorporates techniques such as retrieval-augmented generation (RAG). RAG combines private datasets with large language models to retrieve and integrate contextually relevant information, enabling the models to generate more accurate and useful outputs than they could on their own.
Why enterprises need RAG in their AI data strategies
RAG systems empower enterprises to tap into advanced AI use cases by enhancing both generative and agentic AI capabilities—where “agentic” refers to an AI workflow’s ability to make independent, informed decisions. Essentially, RAG boosts an AI model’s performance by dynamically retrieving and integrating the most current information, supplementing its static training data.
Consider the prompt: “What is the capital of Indonesia?” Traditionally, a model might answer “Jakarta” based on historical data. However, with plans underway to relocate the capital to Nusantara, outdated training data can lead to inaccuracies. RAG systems counteract this by incorporating real-time data retrieval—often via APIs or live data queries—to provide responses that reflect the latest developments.
In practical terms, imagine a pharmaceutical researcher using an AI model to simulate drug performance. If a crucial research paper was published after the model’s training, a RAG system can identify and extract the relevant insights, ensuring the model’s output remains accurate and actionable. Moreover, integrating RAG into an enterprise’s private AI strategy allows secure inference on proprietary datasets, reducing exposure risks while enhancing overall decision-making accuracy. Of course, it’s important to recognize that GPUs remain essential for accelerating real-time inference and fine-tuning, but only after data is properly optimized.
What does a RAG data strategy look like?
When enterprises design a comprehensive AI strategy, their AI infrastructure must be fully integrated with their broader IT ecosystem. Data generated across various applications is rarely in a form that’s immediately consumable by AI workloads. Instead, it requires processing—through normalization, deduplication, tokenization, masking and encryption—to ensure that only clean, accurate and compliant data enters RAG systems.
Preparing data for RAG is just one piece of an AI-ready strategy. Beyond processing, enterprises must centralize their AI-related data to break down silos and ensure seamless communication between distributed data sources. A centralized, scalable environment is essential so that all data—whether originating from public clouds, GPU as a Service providers, or on-premises systems—can be efficiently accessed and utilized throughout the AI lifecycle.
For many IT leaders, the immediate reflex is to deploy AI infrastructure in the public cloud due to its flexibility and scalability. However, relying solely on public cloud storage can restrict an enterprise’s ability to optimize costs and maintain control over its data in the long term.
Instead, it may be better for them to implement a dedicated private storage environment—something that we at Equinix call an Authoritative Data Core. As shown below, an Authoritative Data Core is surrounded by agile interconnectivity that connects it to cloud providers, service providers, and edge data centers, thus enabling a hybrid multicloud approach to AI. Unlike cloud native storage, an Authoritative Data Core allows enterprises to maintain control over their data and ensure privacy. This strategy not only preserves data privacy but also consistently supports the data management policies necessary for effective RAG implementation.
Manage AI data on the right platform
The ideal place to build an Authoritative Data Core is on a neutral platform that offers easy access to partner ecosystems and distributed infrastructure. This is exactly what Equinix offers.
Equinix customers can access our global data center platform to stand up their AI data architecture wherever they need it. They can tap into our industry-leading portfolio of cloud on-ramps to connect to the cloud services that take their AI practice to the next level—all without having to sacrifice custody over their data. Finally, they can use Equinix Fabric® for agile interconnection capabilities to ensure an AI-ready data pipeline that spans the globe.
Also, once they’re ready to incorporate GPUs into their AI strategy, Equinix can help them access ecosystem partners like NVIDIA and build the infrastructure needed to optimize GPU utilization.
High-performance data centers from Equinix can help customers tackle their biggest challenges. This includes implementing their AI-ready data strategy, but also other things like ensuring scalability and high performance, managing costs, meeting sustainability goals and complying with regulatory requirements. Read the infographic to learn more: Shape your future with the data infrastructure your business needs.
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