The rapid growth of AI data—across training and inference workloads and all points in between—is increasing demand for high-performance compute infrastructure. Further, complex and data-intensive AI applications require hybrid multicloud connectivity to enable faster data transfers between critical workloads.
Enterprises are struggling with outdated on-premises data centers that lack compute capacity and power, high-density cooling capabilities and the scalable infrastructure required to support AI workloads. They’re navigating a complex AI landscape driven by specific business needs and data residency, privacy and sovereignty considerations. While public clouds may be an option for hosting AI projects, concerns about privacy, vendor lock-in and unpredictable costs often outweigh the benefits for enterprises.
AI-ready data centers provide the high-performance compute infrastructure and secure network connectivity enterprises need to support high-density, power-intensive AI workloads and accelerate model training, AI inference and data movement. Enterprises can ensure the scalability, reliability and efficiency of their geographically dispersed AI infrastructure while optimizing costs and maintaining robust security and compliance.
What are 3 primary types of AI workloads?
Since definitions can often vary for terms associated with emerging technologies, we’ll start by describing the three primary workloads that AI infrastructure supports.
AI model training involves applying algorithms to large, diverse datasets to establish pattern recognition and extract meaningful insights. This enables the creation of models that can autonomously make decisions or predictions and execute tasks with little or no human intervention. Examples include automating customer relationship tasks and detecting signs of malware.
- AI model tuning is a subset of model training that takes a large, pre-trained AI model and fine-tunes it with additional task-specific data to create a customized version of the original AI model tailored to a specific job, industry or context. For example, the tuned model might adopt a unique tone of voice or learn the intricate details of an industry like legal services or financial reporting.
AI inference introduces current data to an AI model to draw new conclusions without including examples of the desired results. It involves making predictions based on the pattern recognition established during the training phase. For example, a model trained on past market performance can infer future market performance. Or AI inference can be used for fraud detection. Accessing and using the most current data available is crucial. This means that the AI model must be positioned close to users at the digital edge to achieve the most accurate inference.
- Retrieval-augmented generation (RAG) is an approach that more and more enterprises use to improve the accuracy of generative AI query results by using more up-to-date data. Instead of retraining the model, this technique augments the model by querying a vector database, which improves the output by providing more relevant context data as part of the input prompt.
Data in motion refers to the transfer and exchange of data required to populate a model with the right amount of relevant data. This includes moving data to and from training and inference workloads. Data in motion is critical to the improvement of models over time. As data is inferenced, that data can also be fed back into future versions to create a continuous feedback loop. The data may be distributed within regions, across multiple clouds or acquired from AI ecosystems, including AI marketplaces.
Meeting AI workload requirements with flexible AI infrastructure
Each of the workloads discussed above requires AI infrastructure deployed inside high-performance data centers. For example, AI model training, inference and data in motion workloads need the following infrastructure components:
- Scalable multicloud network connectivity for fast data transfers and private connections to service providers, customers, partners and AI ecosystems
- Advanced cooling capabilities, including direct-to-chip liquid cooling, to manage excess heat generated from high-density compute
- Cloud on-ramps to major cloud services providers for seamless connectivity with multiple providers in multiple locations
- High-performance AI environments located close to where data is generated enable customers to maintain ownership over their data and comply with data sovereignty and privacy requirements
AI model training workloads require access to GPUs for powerful compute capacity and reliable advanced power supplies.
AI inference workloads can run on less powerful and more cost-efficient GPUs and CPUs that are typically deployed in edge colocation data centers, to ensure proximity to users and data sources.
Other value-add services for AI infrastructure include:
- Managed services partnerships that help enterprises scale their infrastructure operations to the level of AI performance they need to develop and run massive models. These services also streamline the installation and operation of privately owned infrastructure and service deployment in AI-ready colocation data centers.
- Real-time environment monitoring provides power draw, environmental, mechanical and electrical operating data. This is especially important for liquid-cooled environments where the cooling distribution unit (CDU) flow rates and temperatures need to be actively monitored and serviced.
Running workloads in the right type of AI-ready data center
Once enterprises identify their AI infrastructure requirements, the next step is deploying in an AI-ready data center environment with the best-fit compute, cooling, network and storage capabilities. Depending on the workload size, enterprises can run their workloads in AI-ready data centers, including hyperscale, colocation and edge data centers.
Which data centers are right for model training?
AI model training workloads are not latency-sensitive, so hyperscale or colocation data centers can support these workloads.
Cloud and SaaS providers that are training large language models (LLMs) with massive datasets can benefit from the high capacity of hyperscale data centers. These facilities tend to be in remote areas, where energy and real estate prices are lower, so they can offer cost-efficient space and power.
Traditional colocation data centers located in densely populated metros are well-suited for workloads with midsized capacity requirements, including enterprises training models for their own private use.
Which data centers are right for AI inference?
AI inference workloads require the most recent data available—often real-time data—especially when linked to predictive analytics or high-frequency market transactions. Low latency may be critical for applications such as autonomous driving to achieve the most accurate inference. However, for a chatbot, latency is linked to what is necessary for interactions with humans, making it less critical. Another consideration is whether it’s more cost-effective to position GPUs and CPUs at the edge, rather than backhauling data to a metro data center. Therefore, enterprises must deploy infrastructure in data centers at the edge, close to where the data is generated.
How can data centers support data in motion?
Data in motion workloads involve data moving securely back and forth between different data centers and cloud providers. Therefore, it’s essential for enterprises and service providers to choose data centers that offer robust and secure connectivity options to ensure their data never has to traverse the internet, including when connecting to public clouds. Regardless of whether it’s hyperscale or colocation, a data center can’t truly be AI-ready unless it’s connected to other data centers.
How interconnection puts data in motion, privately and securely
When enterprises transfer their data to a central place for training on infrastructure they own or exchange data with other businesses, they want to do so privately. Interconnection is a virtual networking capability that makes the private transfer and exchange of data possible by connecting enterprises, partners, customers, employees and other entities simultaneously.
Equinix Fabric®, our virtual networking solution for interconnection, makes it quick and easy for customers to connect AI workloads running in different locations. They can connect to partners and service providers in AI ecosystems, including AI marketplaces, which offer additional data sources enterprises can use for AI model training.
Deploying AI infrastructure in AI-ready Equinix IBX® colocation data centers that are strategically located in 70+ metros on six continents enables faster AI model training and deployment and allows data to remain within country for compliance with data sovereignty and privacy regulations. Our vendor-neutral platform and low-latency cloud on-ramps allow seamless hybrid multicloud connectivity for the flexibility of AI workload placement in multiple locations.
With the help of high-performance data centers from Equinix, enterprises are reducing latency, increasing cost-efficiency and future-proofing their operations. To learn more, read the IDC Vendor Profile: Equinix Experiences Strong Growth Driven by AI, Hyperscale, and Digital Infrastructure.[1]
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[1] Courtney Munroe and Avinash Naga, Equinix Experiences Strong Growth Driven by AI, Hyperscale, and Digital Infrastructure, IDC Vendor Profile, May 2024, IDC #US50186623.
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