How Will Digital Infrastructure Enable Private AI?

To get AI right, you need to prioritize data architecture, private network connectivity and sustainability—and ecosystems help too

Jon Lin
How Will Digital Infrastructure Enable Private AI?

It’s no secret that business leaders are energized by the possibilities of generative AI. The dialogue around it is intense and there’s an urgency to figure out how it can help their companies. They have questions around where to start, how to scale and how to harness the knowledge from public AI models while protecting sensitive information such as IP.

That’s where enterprise-level AI comes into play with its combined use of private AI and data and training models in the public cloud. Private AI gives companies the control to capitalize on the benefits of AI while keeping their sensitive data safe and confidential. Like private cloud, private AI must be operational in non-public environments so businesses can use their proprietary data while retaining complete control.

Leaders who have transformed digitally are now deploying their digital infrastructures to advance enterprise AI. These leaders follow three critical strategies to get AI right:

  1. Structure data architectures for governance, privacy and residency
  2. Use interconnection for hyperconnectivity
  3. Choose sustainable AI and use AI for sustainability

They also choose the ecosystems that connect companies with the partners, systems and tools they need to deploy their enterprise AI infrastructure in the right places.

The Equinix Indicator

In the first volume of The Equinix Indicator industry experts share their thoughts on Digital Infrastructure and Private AI.

Learn More
The indicator

Benefits of private AI

Private AI is AI built for an organization’s exclusive use while maintaining control over its models and data. Additional benefits of private AI include:

  • Improving workload latency
  • Reducing regulatory risk
  • Cost predictability

Integrating a hybrid multicloud infrastructure for private AI simplifies access to multiple cloud and edge environments, to run specific workloads.

Let’s take a closer look at the three essential strategies for deploying digital infrastructure that help companies enable private AI.

Structure data architecture for governance, privacy and residency

Private AI requires lots of data from multiple sources—private, public and third party—to create better business outcomes. Once companies build the necessary data lakes and deploy infrastructure to feed data into AI engines, they must format that data for easy consumption.

Private data can be exposed through leakage when using training models hosted in a public AI infrastructure. The right data architecture helps companies maintain control and ownership over their data and supports compliance with data sovereignty and regulatory requirements. It also optimizes costs by reducing the need for data duplication across multiple cloud service providers.

Data architectures should be designed for the seamless flow of information with three crucial components:

  • Governance: Establish processes for collecting, storing, processing and managing data to ensure data quality and regulatory compliance.
  • Residency: Determine where to store specific data sets—on premises, in colocation data centers or in the cloud—and in what country.
  • Privacy: Safeguard data to protect sensitive information and meet global data protection regulations.

Private AI requires new data architectures and patterns that accommodate innovative and best-in-class technologies. Getting data architecture right starts with building an authoritative data core so data can move from the edge to the cloud and back with complete control. This helps companies strike the right balance between deploying their data to multiple AI-related services and maintaining data control.

Use interconnection for hyperconnectivity

Private AI amplifies connectivity requirements, demanding low latency for workloads that require real-time processing. Deploying a hybrid multicloud environment with virtual private network connections—what we call interconnection—shifts data sharing off the public internet to private networks, and seamlessly connects workloads and data worldwide. Interconnection provides hyperconnectivity to and from the digital core, ecosystems and the edge, helping enterprises reach more participants, partners and services, from clouds to data marketplaces.

Private interconnection supports data architecture movement patterns that include:

  • Enabling data ingestion from multiple sources
  • Optimizing data transfer speeds between cloud and private resources
  • Accelerating the distribution and automation of real-time actionable insights

Using interconnection and cloud adjacent storage helps ensure edge access to data, a secure perimeter and real-time inference.

Choose sustainable AI and use AI for sustainability

Private AI requires more computing power, so sustainability is a primary concern over how companies are consuming or building private AI infrastructure.

Sustainable AI integrates environmental efficiencies in the design, development and deployment of AI systems across their life cycle. Sustainable AI includes making algorithms, models and forecasts more energy efficient. To achieve this, companies will look at how colocation data centers provide highly efficient infrastructure and invest in green energy.

More powerful IT equipment, higher density deployments, edge computing and the demand for greater efficiency are all driving the need for more advanced cooling techniques. Advanced liquid cooling technologies—like direct-to-chip—enable businesses to more effectively cool the powerful, high-density hardware that supports compute-intensive workloads like AI.

Data center operators are also exploring other ways to reduce total energy consumption without impacting the safe operation of IT infrastructure, such as adopting the recent guidance from the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), stating that A1 (enterprise level) class equipment can now safely operate at higher temperatures.

Conversely, businesses are using AI to reduce operational carbon emissions and accelerate climate action programs. For example, AI is helping airlines optimize flight paths to reduce fuel usage. Biodiversity companies are using it to uncover more nature-friendly solutions.

Running private AI on Platform Equinix®

Equinix helps businesses advance private AI. We hold a distinctive position in the digital infrastructure landscape as a vendor-neutral platform where companies design their multicloud infrastructure and collaborate with ecosystem partners. We’ve put ourselves at the nexus point of digital infrastructure for the past 25 years, and AI brings an incredible new set of use cases to help build on top of that.

Check out The Equinix Indicator, for conversations we’re having with Equinix executives and industry experts on approaches and best practices to help IT leaders navigate the challenges of going all-in on private AI.

Avatar photo
Jon Lin Executive Vice President and General Manager, Data Center Services
Subscribe to the Equinix Blog