The Weight of AI Data
AI is often associated with sleek algorithms, GPU clusters, and high-powered models like ChatGPT or DALL·E. But the real powerhouse behind AI isn’t just compute — it’s data. Vast, sprawling, complex data.
Training a language model, computer vision system, or recommendation engine requires more than just high-performance processing. It demands seamless access to petabytes of structured and unstructured data. And in 2025, that data is increasingly scattered — across on-premise servers, hybrid environments, enterprise databases, and edge nodes. This shift is pushing AI infrastructure away from traditional hyperscale clouds and toward colocation facilities where data already resides.
This phenomenon, known as data gravity, is rewriting the rules of AI architecture. In this blog, we’ll explore what data gravity means in the context of AI, why colocation sites are becoming the gravitational centers for model training, and how businesses are rethinking their infrastructure to stay ahead.
What Is Data Gravity?
The term “data gravity” was coined by Dave McCrory in 2010 to describe how data attracts services and applications in much the same way gravity attracts objects in space. The more data accumulates in one place, the more likely compute and services will move toward it.
In traditional cloud-first approaches:
- Data was sent to centralized hyperscale clouds.
- AI models were trained where compute power was cheap and abundant.
- Connectivity to cloud-native storage and services was seamless.
But that model is cracking. Today’s enterprise data:
- Is spread across multiple physical and virtual locations.
- Is costly to move due to bandwidth, latency, and compliance limitations.
- Requires location-sensitive handling to meet global data residency laws.
The result? Enterprises are increasingly relocating their compute to where the data lives — in colocation data centersclose to users, systems, and edge devices.
Why AI Training Is Shifting to Colocation
1. Data Proximity to Enterprises
Many enterprise datasets — especially those critical to business operations — are still stored outside the cloud:
- In legacy storage systems like NAS/SAN.
- In on-premise environments with strict security protocols.
- Inside colocation facilities near headquarters or core markets.
Moving petabytes of such data to the cloud isn't always feasible. It’s time-consuming, expensive (thanks to egress fees), and poses security risks. As a result, organizations are choosing to colocate AI compute nodes near their existing data environments.
By doing so, they:
- Minimize data transfer latency.
- Avoid steep cloud transport costs.
- Ensure greater control over data governance and access.
2. Hybrid and Multi-Cloud AI Architectures
Modern AI systems are rarely confined to a single cloud. They span:
- On-prem data lakes storing years of transactional logs or sensor data.
- Edge devices feeding in real-time information.
- Multi-cloud databases and APIs powering inference.
Colocation environments provide the neutral ground between these systems:
- They offer direct onramps to cloud platforms like AWS, Azure, and GCP.
- They allow fiber connections to on-prem networks.
- They support flexible architectures that avoid vendor lock-in.
This flexibility is key to building high-performance AI pipelines that are resilient, scalable, and cost-efficient.
3. Bandwidth and Latency Optimization
Training AI models — especially generative models or reinforcement learning systems — involves intense back-and-forth between storage and GPUs. When compute and data reside in different locations, this creates:
- Bottlenecks in data throughput.
- Increased training times.
- Greater energy consumption.
Colocation sites mitigate this by offering:
- Low-latency fiber connections between storage and compute.
- Direct links to CDNs and ISPs.
- Cross-connects that reduce reliance on public internet pathways.
For real-time inference workloads, like fraud detection or industrial automation, this edge-level performance is crucial.
4. Compliance and Data Sovereignty
Global privacy regulations like GDPR, HIPAA, and CCPA now shape how and where data can be stored and processed. Training AI on sensitive data often requires:
- Localized infrastructure within certain countries or regions.
- Strict control over hardware and access.
- Full auditability of the environment.
Colocation empowers compliance by:
- Allowing enterprises to choose specific facilities in designated geographies.
- Enabling full-stack control over compute resources.
- Supporting certifications like ISO 27001, SOC 2, and PCI DSS.
This makes it possible to build compliant AI pipelines without handing over sensitive data to third-party public cloud vendors.
How Colocation Providers Are Powering AI
Colocation companies are rapidly evolving to support AI workloads. Here’s how industry leaders are adapting:
Equinix
- Offers AI-ready cabinets supporting high-density GPU deployments.
- Delivers liquid cooling infrastructure and high-wattage power per rack.
- Connects directly to NVIDIA DGX Cloud and GPU-as-a-service providers.
Digital Realty
- Builds data-centric campuses designed for AI and ML operations.
- Offers direct links to public clouds and edge nodes.
- Provides high-density zones optimized for training and inference.
QTS
- Delivers GPU-optimized suites with 30kW+ per rack.
- Pairs with renewable energy sources to lower AI’s carbon footprint.
- Supports physical security and compliance for sensitive workloads.
Cyxtera
- Offers bare metal as a service (BMaaS) geared for AI deployment.
- Allows customers to bring or lease GPU servers.
- Integrates with orchestration tools for easy model management.
The Architecture of AI Training in Colocation
A typical AI setup inside a colocation facility includes:
- GPU Servers (NVIDIA A100, H100, etc.) – Core compute engines for model training and inference.
- NVMe Storage Arrays – To handle massive throughput with sub-millisecond latency.
- Fiber Interconnects – To integrate with enterprise data sources or public clouds.
- Liquid Cooling Systems – To manage thermal loads from dense GPU deployments.
- Orchestration Platforms (Kubernetes, Nomad) – To distribute workloads across clusters.
- Infrastructure-as-Code Tools (Terraform, Crossplane) – To automate provisioning.
This hybrid model offers cloud-like flexibility while preserving proximity to mission-critical data.
The Edge and Inference Advantage
AI models aren’t just trained once — they’re continuously used in inference pipelines that:
- Power personalized recommendations.
- Detect fraud in financial transactions.
- Enable autonomous systems in smart cities and vehicles.
These inference workloads demand:
- Predictable latency.
- Local compute resources.
- Scalability on demand.
Edge-focused colocation facilities (in cities like Dallas, Singapore, and Frankfurt) are becoming the go-to inference hubs, combining proximity to data with robust interconnection and hardware availability.
Challenges to Consider
While colocation unlocks many AI advantages, it’s not without challenges:
Power and Cooling Limits
- AI workloads require dense racks (30kW+), which many legacy facilities weren’t designed to support.
- Operators are racing to upgrade electrical infrastructure and adopt immersion or direct-to-chip liquid cooling.
Hardware Investment
- High-performance GPU servers are expensive.
- Enterprises are turning to options like fractional leasing, GPU-as-a-Service, or BYOH (Bring Your Own Hardware) models offered by some colocation providers.
Data Governance
- Moving sensitive data into colocation for AI requires stringent controls:
- End-to-end encryption
- Role-based access
- Anonymization where needed
These challenges must be met with planning, budget alignment, and the right provider partnership.
The Colocation Future of AI
Colocation is no longer just about space and power — it’s a strategic enabler of AI success. By colocating compute near data, businesses are:
- Training faster, smarter, and cheaper.
- Complying with data laws and industry regulations.
- Building resilient, vendor-neutral AI pipelines.
- Reducing cloud costs without compromising scale.
In 2025 and beyond, data gravity will dictate AI gravity. And colocation will be where the most intelligent systems take root.
