AI Is Redefining What’s Possible. Is Your Infrastructure Ready?

Within the last decade, AI has gone from an emerging technology to a business imperative. Now, the development of new no-code and low-code platforms promises to send the growth of AI into overdrive. In the future, AI applications will no longer have to be built by dedicated developer teams with specialized skill sets. Anyone with an idea for how to solve a business problem will be able to create their own AI application using an intuitive drag-and-drop web editor.

Although no-code platforms are still in their early days, users have already begun experimenting with them to do everything from helping ALS patients control lights with their facial expressions[1] to using IoT devices for smart agriculture on an industrial scale.[2]

As these platforms increase in adoption in the years to come, they will help drive AI technology further into the mainstream. However, AI models are only as good as the data we feed into them. To use AI applications to their full potential, enterprises need optimized digital infrastructure that can move the right data to the right place at the right time.

Groundbreaking technology requires groundbreaking infrastructure

When it comes to IT infrastructure, AI is inherently different from anything that’s come before. AI applications are supported by a diverse and incredibly complex set of underlying components, including semiconductors, platforms, clouds, network infrastructure, tools and frameworks, and complex capabilities like natural language processing (NLP) and video analysis.

General-purpose IT components, no matter how advanced, simply can’t account for this level of complexity. In fact, a survey from ESG found that 98% of AI adopters said they identified or anticipated a weak component somewhere in their AI infrastructure stack.[3] The survey found no single weak spot was named significantly more often than others; responses were fairly well distributed across processing, data storage, networking and more.

AI has unique infrastructure requirements because of the massive amount of data involved. To create accurate AI models, enterprises must ingest extremely large volumes of data, harvested from many different sources, and shared across a diverse ecosystem of partners and service providers.

For instance, trucking companies using AI for predictive maintenance of tires need to account for many different factors to ensure their models are sufficiently accurate. This would include not only the miles driven on each set of tires, but also the weather in which the tires were driven, the condition of the roads they were driven on, and various other factors that might impact the rate of tread wear. This example shows the diverse data sets needed to support even the simplest of AI use cases. Applications incorporating advanced functions like NLP use even larger and more complex data sets, amplifying the infrastructure requirements even further.

Processing all that data requires significantly higher levels of compute power than conventional workloads, which in turn creates infrastructure management challenges. The accelerated computing clusters needed to power AI use significant amounts of energy; enterprises should focus on sourcing renewable energy to ensure they’re doing AI in a sustainable manner.

In addition, accelerated computing systems need reliable cooling techniques to ensure they continue functioning properly. The cooling needs of AI workloads may be beyond the capabilities of the air-cooling methods traditionally used for conventional IT workloads. Enterprises will likely need high-density cooling methods such as liquid cooling instead.

Finally, digital infrastructure for AI needs to be highly reliable. This means that enterprises need to ensure the individual components that make up their physical infrastructure—including the power supply, cooling systems, networking equipment and more—are all highly reliable.

Overcoming costs and complexity in your AI infrastructure stack

To avoid falling into the trap of half-finished AI projects, enterprises need the right infrastructure, but they must also deploy that infrastructure in the right places. The sheer volume of data involved makes it infeasible to support AI workloads using traditional centralized IT infrastructure.

For many businesses experimenting with AI for the first time, deploying in the public cloud may seem like the ideal way to get started. After all, the public cloud provides a low barrier to entry, with access to resources on demand and no up-front CAPEX. However, as data growth continues to accelerate and cloud environments scale, cost will inevitably become an issue.

AI development is a very repetitive process; to stay within acceptable levels of accuracy, models must be retrained frequently using new, larger data sets. This leads to greater storage and compute requirements, not to mention higher backhaul costs for data movement. As these costs add up, many enterprises realize that cloud-first AI is not as cost-effective as they once thought.

Go from cloud-first to hybrid AI with Equinix

A hybrid approach to AI lets companies take advantage of the simplicity and scalability of public cloud for some workloads, without getting locked into high costs for every aspect of their AI infrastructure. Leveraging Platform Equinix®, organizations can take a cloud-adjacent approach, where they use cost-effective on-premises infrastructure to support the bulk of their AI model training. At the same time, they align that on-premises infrastructure to be proximate to cloud on-ramps from their chosen providers, allowing them to tap into cloud services as needed and scale during periods of peak demand.

The global footprint of Platform Equinix helps organizations host AI workloads at the digital edge. This is key because the data needed to support AI workloads is largely generated by end users and devices residing in edge locations. As the volume of those data sets increases, it becomes more efficient and cost-effective to bring the compute infrastructure to the data, rather than moving the data back and forth to the compute. In addition, avoiding long-haul data transfers helps keep latency low, which allows AI models to react to events in near-real time.

Equinix colocation and digital services also help create the data sharing ecosystems that are so essential to AI applications. Equinix Fabric™, our software-defined interconnection solution, allows organizations to create virtual connections with their distributed data sources—wherever those sources may be located. In addition, with about 10,000 businesses already part of the Equinix digital ecosystem, there’s a good chance any organization you need to connect with already has a presence at Equinix.

Equinix Metal™ offers dedicated compute and storage on demand using a Bare Metal as a Service model. Enterprises can use Equinix Metal to accelerate their AI inferencing deployments. The solution enables an end-to-end AI approach, where enterprises can support everything from development to deployment on Platform Equinix.

NVIDIA and Equinix partner to streamline hybrid AI infrastructure

One example of how Equinix is working to make AI easier for our customers is our collaboration with NVIDIA. NVIDIA LaunchPad on Platform Equinix provides a cost-effective way for enterprises to fast-track their AI projects.

The program provides a full stack of infrastructure with every component purpose-built for AI, so you don’t have to worry about there being a weak link anywhere in the chain. It includes NVIDIA DGX Foundry, which provides high-performance core infrastructure for AI training, without enterprises having to deploy or manage that infrastructure for themselves. The stack also includes distributed edge infrastructure for model inferencing, and Equinix Fabric to provide secure, dedicated connectivity between training and inferencing infrastructure.

NVIDIA LaunchPad is available to try at no cost. Customers who like their experience can deploy the NVIDIA AI capabilities at Equinix as they need to scale their solution. As their AI models grow larger and more complex in the future, they can grow their infrastructure accordingly using a convenient managed services model.

To learn more about NVIDIA LaunchPad on Platform Equinix and how it can help address the challenges of AI infrastructure complexity, read the ESG white paper “Enable AI at Scale with NVIDIA and Equinix.”

You may also be interested in

Check out the on-demand session “AI/ML Solutions for Large-Scale Development and Deployment” from NVIDIA GTC. In this joint Equinix and NVIDIA session, you’ll learn about new consumption models for AI infrastructure that help speed time-to-insight.

[1] The New York Times, “‘No-Code’ Brings the Power of A.I. to the Masses.” Craig S. Smith, March 15, 2022.

[2] Data Center Knowledge, “AI Workloads Require Infrastructure Upgrades.” Salvatore Salamone, May 17, 2022.

[3] ESG Master Survey Results, Supporting AI/ML Initiatives with a Modern Infrastructure Stack, May 2021.