What Are AI Workloads?

AI workloads are the computational tasks that artificial intelligence systems perform, everything from training models on massive data sets to running real-time inference at the edge. They span data preprocessing, model training, inference, natural language processing, computer vision, and generative content creation. Each places distinct demands on compute, storage, and networking infrastructure.

According to IDC, global spending on AI infrastructure will reach $758 billion by 2029. Gartner projects worldwide AI spending will hit $2.5 trillion in 2026. Organizations across every industry now depend on AI workloads to drive decisions, automate operations, and stay competitive.

But running these workloads well is harder than adopting them. The gap between a working AI prototype and a production-ready system often comes down to infrastructure—whether the underlying storage, compute, and data pipelines can keep pace with the demands AI places on them. 

This article breaks down the types of AI workloads, their infrastructure requirements, the challenges organizations face in managing them, how to secure and govern them, and practical strategies for building systems that perform at scale.

How AI workloads have evolved

The concept of AI workloads is not new, but their scale and complexity have changed dramatically over the past decade.

Early machine learning workloads in the 2010s were relatively modest, training a classification model on structured data sets that fit in memory, running on CPU clusters. The rise of deep learning changed that equation. Neural networks with millions of parameters required GPU acceleration, and data sets grew from gigabytes to terabytes.

The rise of large language models (LLMs) introduced new infrastructure requirements. Training GPT-scale models can require thousands of GPUs running in parallel for weeks, consuming petabytes of text data. Training costs for the largest models can exceed $100 million. As a result, AI workloads increasingly require data center architectures designed for large-scale compute, storage, and networking.

By 2026, inference workloads are projected to account for 55% of AI-optimized IaaS spending and exceed spending on training-intensive workloads, according to Gartner. Unlike training, inference runs continuously to support real-time applications such as chatbots, recommendation engines, and fraud detection systems. This shift increases the need for infrastructure that can support reliable, low-latency inference.

Types of AI workloads

AI workloads break down into several distinct categories. Each has different compute, storage, and networking profiles, and understanding these differences is critical for designing infrastructure that performs well.

Data preprocessing

Before any model can train, raw data must be collected, cleaned, labeled, and transformed into a usable format. This stage, often called the data pipeline, is where AI projects spend most of their time. Data preprocessing workloads are storage- and I/O-intensive, involving heavy reads and writes across distributed file systems. Tasks include ETL (extract, transform, load) operations, feature extraction, data deduplication, and format conversion.

Model training

Training is the process of teaching an AI model to recognize patterns by exposing it to large data sets and iteratively adjusting its internal parameters. Training workloads are the most compute-intensive category of AI work:

• They require specialized hardware, primarily GPUs or TPUs, running in parallel across clusters.
• A single LLM training run can take weeks on thousands of accelerators.
• Storage must deliver sustained, high-throughput sequential reads to keep GPUs fed.
• High-speed networking (InfiniBand or RDMA over Ethernet) connects nodes in the training cluster.

Model inference

Inference is the process of using a trained model to make predictions or generate outputs on new data. While inference requires less raw compute than training, it has stricter latency and availability requirements because it runs in production, often serving end users directly.

Real-world inference examples include recommendation engines serving product suggestions, fraud detection systems scoring transactions in real time, and chatbots generating conversational responses. According to McKinsey research, inference workloads are projected to account for more than half of all AI compute by 2030.

Deep learning workloads

Deep learning workloads involve training and deploying neural networks with multiple layers of artificial neurons. These workloads are a subset of machine learning but are significantly more demanding—they require powerful AI accelerators and high-bandwidth memory. Image recognition, speech processing, and autonomous vehicle perception systems all run on deep learning models.

Natural language processing (NLP)

NLP workloads enable AI systems to understand, interpret, and generate human language. These tasks include sentiment analysis, translation, text summarization, and conversational AI. NLP workloads can range from lightweight models running on CPUs to massive transformer-based architectures that require GPU clusters for both training and inference.

Generative AI workloads

Generative AI workloads produce new content—text, images, video, code—based on training data and user prompts. These include large language models, diffusion models for image generation, and multimodal systems that work across content types. Generative AI workloads are among the most resource-intensive, requiring large-scale GPU clusters for training and low-latency serving infrastructure for inference.

Computer vision

Computer vision workloads enable machines to interpret visual data from cameras, LiDAR, and other sensors. Applications include medical image analysis, quality inspection in manufacturing, facial recognition, and autonomous navigation. These workloads demand high-throughput data ingestion and parallel processing to handle image and video streams in real time.

AI workloads vs. traditional workloads

AI workloads differ from traditional enterprise workloads in several fundamental ways. Understanding these differences can help organizations plan infrastructure that meets AI-specific demands rather than trying to force-fit existing systems.

The core takeaway: Traditional storage and networking architectures were not built for the I/O patterns, data volumes, and parallelism that AI workloads demand. Organizations that try to run AI on legacy infrastructure quickly hit bottlenecks—starved GPUs, slow data pipelines, and increasing costs.

Industry applications of AI workloads

AI workloads are reshaping operations across nearly every sector. Here are some of the highest-impact applications:

Healthcare

In healthcare, AI workloads power diagnostic imaging tools that help detect diseases like cancer from radiology scans, predict patient outcomes from electronic health records, and accelerate drug discovery by modeling molecular interactions. These applications require high-throughput storage for medical imaging data sets that can reach petabyte scale.

Financial services

Financial institutions use AI workloads for real-time fraud detection, credit risk modeling, algorithmic trading, and regulatory compliance automation. Inference workloads in finance demand sub-millisecond latency; every microsecond of delay in transaction scoring can represent potential exposure.

Manufacturing

AI-driven quality inspection, predictive maintenance, and supply chain optimization rely on inference workloads running at the edge—close to the production line. Training workloads process sensor data collected from industrial IoT devices across factory floors.

Retail

Retailers deploy AI workloads for personalized recommendations, demand forecasting, dynamic pricing, and inventory optimization. These applications analyze consumer behavior patterns in real time, requiring both high-throughput data processing and low-latency inference.

Challenges in managing AI workloads

Running AI workloads in production introduces a set of challenges that traditional IT operations are not equipped to handle.

• GPU scarcity and cost. GPUs and other AI accelerators remain expensive and often supply-constrained. A single GPU can cost over $30,000, and training large models requires hundreds or thousands of them. Efficient resource allocation, ensuring GPUs stay busy rather than idle, is a constant balancing act.
• Storage bottlenecks. When storage cannot deliver data fast enough, GPUs sit idle waiting for their next batch. This "GPU starvation" problem is one of the most common and costly inefficiencies in AI infrastructure. Storage systems must deliver sustained high throughput for training and low-latency random I/O for inference.
• Data management complexity. AI workloads consume vast volumes of unstructured data that must be collected, cleaned, versioned, and governed across distributed environments. Maintaining data quality and lineage across the AI pipeline is a significant operational challenge.
• Scaling infrastructure. As models grow larger, data sets expand, and organizations add generative AI to traditional machine learning workloads, infrastructure must scale accordingly. This means not just adding more compute, but scaling storage throughput, networking bandwidth, and orchestration systems in parallel. Scaling vertically (bigger machines) and scaling horizontally (more machines) introduce complexity.
• Cost control. AI infrastructure costs can spiral quickly. Without monitoring and optimization, organizations may overprovision resources during development and underuse them in production. Cloud-based AI workloads are especially prone to cost overruns when GPU instances run without active management.
• Energy consumption. Large-scale AI workloads consume enormous amounts of power. Data center operators increasingly face constraints around power availability and cooling capacity, making energy efficiency a first-order infrastructure concern.

Infrastructure requirements for AI workloads

Building infrastructure that supports AI workloads effectively requires attention to four layers: compute, storage, networking, and orchestration.

Compute

GPUs remain the primary accelerator for AI workloads. NVIDIA's data center GPUs, including the A100, H100, and B200, are widely used for AI training and inference, while Google TPUs and custom ASICs serve more specialized use cases. Field-programmable gate arrays (FPGAs) offer lower-power alternatives for specific inference tasks. The key is matching accelerator type to workload profile. Training favors raw throughput; inference often prioritizes latency and energy efficiency.

Storage

AI workloads need storage that delivers high throughput for training (feeding data to GPU clusters at line speed) and low latency for inference (serving model weights and data quickly). Object storage and parallel file systems are common for training data, while all-flash arrays provide the consistent, low-latency performance that inference requires.

Storage for AI workloads also has to absorb frequent checkpoint writes during long training runs, when the cluster periodically saves model state so a failure does not waste days of compute. It has to sustain high metadata performance, too, because preprocessing pipelines often read millions of small files. When any of these falls short, the symptom is the same: GPU starvation, with expensive accelerators idling while they wait for the next batch of data.

Networking

Distributed training across GPU clusters demands high-bandwidth, low-latency networking. InfiniBand and RDMA-capable Ethernet fabrics (100–400Gbps) are standard for interconnecting nodes within training clusters. Network topology and congestion management directly affect training time and cost.

Orchestration

Kubernetes with AI-specific extensions like Kubeflow and Kueue has become the standard for orchestrating AI workloads. These tools manage job scheduling, resource allocation, scaling, and multi-tenancy across shared GPU clusters. Machine learning operations (MLOps) practices—model versioning, experiment tracking, continuous training, and monitoring—are essential for managing AI workloads in production.

Deployment: Cloud, on premises, and hybrid

Where AI workloads run is a separate decision. Public cloud offers fast access to GPUs without capital outlay, which suits experimentation and variable demand, but sustained training and high-volume inference can make rented GPU instances costly over time. On-premises infrastructure gives predictable economics and data control for steady, large-scale workloads. Many organizations settle on a hybrid model: train or fine-tune where capacity is cheapest, then serve inference close to users for lower latency.

Securing and governing AI workloads

AI workloads concentrate sensitive data and high-value models in one place, which makes them an attractive target and a governance challenge. Securing them means protecting three things at once: the training data, the model itself, and the inference endpoints that expose it to users.

The threats are not the same ones traditional applications face. Training data can be poisoned to corrupt a model's behavior. Model weights, often worth millions to reproduce, can be exfiltrated. Inference endpoints can be probed through prompt injection or adversarial inputs designed to extract data or force unintended outputs. Each layer of the AI pipeline needs its own controls.

A practical baseline covers a few areas:

• Data protection: Encrypt training and inference data in transit and at rest, and apply access controls so only authorized users and services can reach sensitive data sets. Immutable copies of critical data and model checkpoints support recovery if a pipeline is compromised.
• Access and identity: Enforce least-privilege access across the cluster, segment multi-tenant environments, and log who touches models and data for audit purposes.
• Data lineage and governance: Track where training data came from, how it was transformed, and which model version it produced. Lineage matters both for reproducibility and for demonstrating compliance.
• Monitoring: Watch inference endpoints for anomalous queries and model behavior that drifts from expected patterns.

Many organizations anchor these practices to a recognized standard. The NIST AI Risk Management Framework, published in 2023, organizes the work into four functions—govern, map, measure, and manage. It’s increasingly used as a reference point for AI governance as organizations prepare for regulations such as the EU AI Act. Pairing that governance structure with technical controls keeps data protection aligned with how AI systems actually operate.

Best practices for optimizing AI workloads

Organizations that manage AI workloads effectively tend to follow a set of consistent practices:

• Right-size infrastructure to the workload. Training, inference, and data preprocessing each have different compute, storage, and latency profiles. Design infrastructure for the specific workload rather than applying a one-size-fits-all approach.
• Eliminate storage bottlenecks first. Low GPU utilization is one of the costliest inefficiencies in AI infrastructure. If GPUs sit idle waiting for data, other optimizations are irrelevant. Invest in storage that can sustain the throughput your training jobs require.
• Automate resource management. Use orchestration tools to schedule workloads, manage GPU allocation, and scale resources dynamically. Manual provisioning typically cannot keep pace with AI demands.
• Monitor and optimize continuously. Track GPU utilization, storage throughput, network latency, and cost per training run. Use these metrics to identify bottlenecks and right-size resources over time.
• Plan for inference from the start. Many organizations optimize heavily for training and then scramble to build inference infrastructure. Design your architecture to support both from the beginning.
• Implement data governance early. AI workloads depend on data quality. Establish data versioning, lineage tracking, and access controls before scaling your AI pipeline, not after.

The future of AI workloads

Several trends will shape how AI workloads evolve over the next two to three years.

Inference is becoming the dominant workload category. As more models move into production, organizations will spend more on serving models than training them. This shifts infrastructure priorities toward low-latency, high-availability systems optimized for real-time response.

Edge AI is expanding. Running inference workloads on edge devices, autonomous vehicles, factory sensors, and medical instruments reduces latency and bandwidth costs. This requires smaller, optimized models and a distributed infrastructure that extends beyond the data center.

Agentic AI, systems that can plan, reason, and take actions autonomously, is introducing new workload patterns that combine inference with tool use, memory, and multi-step reasoning. These workloads require more dynamic orchestration and tighter integration between compute and data layers.

Energy efficiency is becoming a competitive differentiator. Organizations are adopting techniques like model quantization, pruning, and distillation to reduce the compute requirements of AI workloads without sacrificing accuracy.

Conclusion

AI workloads, from data preprocessing and model training to real-time inference and generative AI, represent the computational engine driving modern enterprise strategy. Understanding the distinct infrastructure requirements of each workload type is the foundation for building AI systems that perform reliably at scale.

The business impact is clear: Organizations that invest in purpose-built AI infrastructure can gain faster time to insight, lower operational costs, and the ability to move AI initiatives from pilot to production without hitting infrastructure walls. As AI workloads continue to grow in scale and complexity, the gap between organizations with mature AI infrastructure and those without will only widen.

Everpure helps organizations build AI-ready infrastructure that can reduce storage bottlenecks that limit AI performance. FlashBlade//S™ delivers the sustained, high-throughput storage that keeps GPU clusters fed during training, while FlashBlade//EXA™ provides the scale-out capacity and metadata performance that modern AI and HPC workloads demand. Everpure partners with NVIDIA to offer full-stack, AI-ready infrastructure that simplifies deployment and accelerates time to results. And with Evergreen//One™, organizations can consume storage as a service, scaling capacity and performance on demand without overprovisioning. Together, these solutions give data teams the infrastructure foundation to focus on building models, not managing storage.