What Are AI Data Centers? | SambaNova

What Are AI Data Centers? | SambaNova

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What Are AI Data Centers?

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SambaNova

--> July 24, 2026

The term "data center" indicates a physical facility that stores servers, networking, and other infrastructure necessary to run IT operations in an organization.

TL;DR

An AI data center is infrastructure optimized for the compute, storage, networking, and power that AI workloads demand, often an existing facility extended with accelerators rather than a purpose-built one.

AI splits into two workloads: training, which teaches a model on historical data in dense liquid-cooled clusters, and inference, which runs the trained model in production inside existing air-cooled facilities.

Most organizations run inference, not training, so being AI-ready means matching accelerators, networking, and cooling to the workload and maximizing tokens per second per watt, not building a new facility.

GPUs dominate model training, purpose-built Reconfigurable Dataflow Units (RDUs) handle inference more efficiently, and CPUs still run the data pipelines, orchestration, and preprocessing around them.

SambaNova's SambaRack delivers independently benchmarked fastest inference on large open-source models with up to 4x the energy savings of GPU-based alternatives, running at 10 kW on standard air cooling.

For decades, this traditional CPU-centric data center model supported enterprise workloads. However, AI is fundamentally changing infrastructure requirements. Training and experimenting with large language models (LLMs) demand massive parallel computation, far beyond traditional enterprise workloads.

Running AI systems at scale ( AI inference ) introduces more challenges. Organizations require infrastructure that can continuously move and process large data volumes while maintaining low latency and predictable performance.

As a result, organizations are extending and transforming their traditional data centers to support AI-native workloads. This article explores key components in an AI data center, who needs them, and the different deployment options available to get started.

Defining ‘AI Data Center’

An AI data center is infrastructure optimized for the compute, storage, networking, and power demands of artificial intelligence (AI) workloads. That can mean a purpose-built facility, but it increasingly means an existing data center extended with the right hardware to run AI efficiently.

At their core, AI data centers pair specialized processors, such as GPUs and Reconfigurable Dataflow Units ( RDUs ), with high-bandwidth interconnects, fast storage, and a power and cooling profile matched to the workload. Alongside these accelerators, CPUs continue to handle data pipelines, orchestration, and preprocessing. Together they deliver the throughput, low latency, and energy efficiency modern AI systems demand at scale.

Building one from the ground up requires substantial capital and technical resources. But that is not the only path, and for AI inference specifically it is rarely the most efficient one. This article explores the alternatives later.

How Are AI Data Centers Used?

As organizations move from isolated pilots to embedding AI across core operations, compute demand is increasing exponentially. What begins as cloud experimentation often evolves into sustained, high-utilization workloads that demand architectural planning, cost optimization, and long-term infrastructure strategy.

As a result, AI data centers are rapidly becoming strategic infrastructure supporting sovereignty, scalability, risk management, and sustained competitive advantage.

Public Sector

Defense and intelligence agencies require sovereign, high-performance compute environments to support advanced analytics, cyber operations, and autonomous systems. They use AI data centers to support national security, resilience, and technological independence.

Similarly, scientific research institutions and universities rely on AI data centers for applications such as climate modeling, genomics, advanced materials, and language research.

Private Sector

Businesses are tapping into the productivity gains and cost savings potential of AI. Adoption has shifted from testing to full-production deployment in organizations worldwide.

Regulated industries, such as financial services and healthcare, must balance AI innovation with strict compliance, data residency, and auditability requirements.

AI data centers allow them to control sensitive data environments while still deploying advanced analytics, fraud detection, personalized medicine, and predictive risk modeling at scale.

What Makes an AI Data Center Different?

The distinction is not traditional infrastructure versus AI infrastructure; the distinction lies in how the workload is processed. Data centers designed for AI split the workloads into two very different jobs because each places different demands on the facility.

Training is where a model learns from historical data. This is the workload most people picture when they imagine AI infrastructure: Thousands of accelerators packed at extreme density, drawing enormous power, often requiring liquid cooling and specially designed facilities. Training clusters are capital-intensive and concentrated among a handful of frontier labs and large operators.

Inference is where a trained model does its job in production, generating predictions, answers, and actions from new data. This is the workload most organizations actually run that grows continuously as AI, and increasingly agentic AI, moves into core operations. Inference does not require a new class of building. With the right architecture, it can run inside existing data centers, alongside the CPUs that still handle data pipelines, orchestration, and preprocessing.

This is where infrastructure strategy comes into play. CPUs remain essential to most AI workloads, but leveraging the right accelerator chip for the right workloads can transform data center performance. By separating the compute-bound prefill workflows with GPUs and memory-bound decode workflows with RDUs, AI data centers can achieve better energy efficiency and better performance without the bottlenecks and latency found with GPU-only implementations.

Making a data center ready for inference at scale comes down to a few requirements:

Accelerators are sized for inference throughput, not just training, so you maximize tokens per second per watt rather than raw peak compute.

Networking...