9 May 2025
8 minutes
AI workloads are collections of individual computer processes, applications and real-time computational resources used to complete tasks specific to artificial intelligence, machine learning and deep learning systems.
More specifically, the term AI workloads refers to resource-intensive tasks requiring large amounts of data processing related to developing, training and deploying AI models.
Behind the scenes, individual AI workloads enable AI applications to simulate human-like characteristics, such as comprehension, decision-making, problem-solving, creativity and autonomy, associated with the ways humans learn, think and make conclusions.
The difference between AI workloads and traditional workloads
Within IT, the term workload has evolved, carrying different connotations in different contexts. Generally, a workload refers to the total system demand, the amount of time and resources, needed to achieve a specified desired outcome. Workloads can range from relatively simple tasks like a single computation or a standalone application to complex operations, such as processing large-scale data analytics, hybrid cloud or public cloud services, or running a suite of interconnected apps and workflows.
As a subset, AI workloads are associated with tasks related to AI applications, such as generative AI (gen AI) large language models (LLM) like ChatGPT, natural language processing (NLP), and running AI algorithms. AI workloads are differentiated from most other types of workloads by their high levels of complexity and the types of data processed. Compared to other types of workloads, AI workloads typically process unstructured data like images and text.
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Broadly, AI workloads can be divided into two main categories, with model training and model inference being the two most encompassing:
Model training workloads are used to teach AI frameworks to identify patterns in order to make accurate predictions.
Model inference workloads are composed of tasks (and their associated computing power) necessary for AI models to interpret and respond to brand-new data and requests.
Drilling down, some additional, more nuanced types of AI workloads include the following:
These types of workloads involve preparing data for either deeper analysis or model training purposes. As a critical step in training AI models, processing workloads ensures that training data meets predefined quality and formatting standards. Data processing workloads contain tasks like extracting and collating data from different sources into a consistent format and then loading the data into a storage system for easy access for the AI model. These types of processes may also include more advanced operations like feature extraction, in which specific data points or attributes are identified as desired inputs from within less structured datasets.
Machine learning (ML) workloads are directly related to the development, training and deployment of ML algorithms used for learning and making predictions. ML workloads process large datasets, iteratively adjusting the model parameters for improved accuracy. ML models are valuable for inference tasks, such as predicting future events based on historic patterns. These types of workloads can be particularly resource-intensive during the training phase, requiring specialized processors like GPUs (graphics processing units) and TPUs (tensor processing units) to accelerate operations through parallel computations.
Deep learning (DL) workloads are used for training and deploying neural networks that mimic the way the human brain thinks, learns and solves problems. As a subset of machine learning, deep learning systems are defined by a greater depth, involving multiple layers of artificial neurons, or nodes, that use increasingly complex data hierarchies to make connections and abstractions. DL models are especially useful for image recognition and speech recognition tasks, but these types of workloads can be even more demanding than ML workloads, demanding the kinds of powerful AI accelerators used in high-performance computing (HPC).
Natural language processing (NLP) tasks help humans interact with AI systems through conversational prompts. These types of workloads help AI models understand and interpret natural language and then generate responses that are easy for humans to understand as well. Tasks associated with NLP include sentiment analysis, language translation and speech recognition. NLP systems need to be able to analyze large volumes of text and audio data for context, grammar and semantics. Modern CPUs (central processing units) are capable of running NLP AI systems, however, more complicated linguistic models may strain standard processors and require higher levels of computational resources.
Generative AI systems are used to produce new content (e.g., text, images, videos) based on vast sets of training data and user prompts. Gen AI workloads interpret user commands and make inferences to create coherent outputs. Large language models use gen AI workloads for tasks like predicting the best next word to use in a sentence. Diffusion models, used for image and video generation, use these types of workloads to iteratively refine random noise into coherent and contextually relevant visuals, almost like a sculptor chipping away at a block of marble.
Computer vision workloads enable computers to use sensors like cameras and LiDAR to interpret visual data, identifying objects and reacting in real time. These types of tasks are critical for applications like self-driving vehicles or automated surveillance. Computer vision workloads include tasks like image classification, object detection and facial recognition.
AI workloads are useful for every type of AI application. Recent breakthroughs in technology have brought AI into a new age of utility, with applications spanning industries from automation to automotive, healthcare to heavy manufacturing. Every day, new AI applications are being tested and previous applications are being refined, with the potential to significantly improve a wide range of services and operations.
Customer experience, service and support
AI-powered chatbots and virtual assistants are increasingly being employed by companies seeking to better handle customer concerns, support tickets and even sales. Tools like these use natural language processing and gen AI to interpret and reply to customers’ questions, providing quick answers or laddering up more challenging inquiries to live agents. Because AI can handle low-level tasks like answering frequently asked questions and providing always-on support, human agents are able to focus more time on high-level tasks, resulting in a better user experience overall.
Fraud detection
AI is quickly becoming an extremely powerful defense against the continuously evolving tactics used by scammers and fraudsters. Machine learning and deep learning algorithms can analyze complex transaction patterns and flag suspicious behavior through anomaly detection. Whereas human fraud detection experts have only limited bandwidth, AI can review exponentially more data by the second, an invaluable tool for industries like banking.
Personalized experiences
Retailers, banks and other customer-facing companies are using AI to create more personalized shopping and entertainment experiences to improve customer satisfaction and prevent churn. AI algorithms can use customer information like personal interests and past shopping data to tailor product and service recommendations towards a customer’s preferences.
Human resources and recruitment
Hiring and managing a qualified workforce can be a tremendous strain on any industry. AI-powered recruitment platforms are helping streamline the hiring process by screening resumes, matching good candidates with open roles, and even conducting preliminary interviews using video analysis. Tools like these can help empower HR professionals to reduce the time spent on minor administrative tasks and focus more on the most promising job applicants. By sorting through large volumes of potential hires, AI helps minimize time-to-hire and reduces response times, improving the experience for applicants, whether they get the job or not.
Application development and modernization
Gen AI tools, which can produce detailed outputs based on conversational props, are demonstrating unique value for coders and developers. Capable of producing runnable code, automated AI tools like these streamline repetitive tasks associated with writing code, help with application development, and accelerate app migration and app modernization initiatives. Although not a replacement for talented coders, AI coding tools can help reduce errors and ensure code consistency.
Predictive maintenance
Paired with powerful tools like virtualization, machine learning models can analyze data collected from sensors, Internet of Things (IoT)-enabled devices and operational technology (OT) to build reliable forecasts for necessary equipment maintenance, preventing machine failure. AI-enabled predictive maintenance can reduce costly downtime and help businesses protect their bottom lines.
Workload management of any type is a critical component of any sizable IT department. Improper configurations can directly impede overall system performance, leading to increased costs, reduced stability and negative user experiences. AI solution providers like IBM, Microsoft Azure, Nvidia and Amazon Web Services (AWS) are constantly looking for cost-effective ways to optimize, reducing bandwidth strain on major pipelines and improving overall performance through the lifecycle of all types of workloads.
While there are many types of complicated workloads, AI workloads can be among the most demanding. They require ample data storage solutions, either on-premises or in remote data centers, and powerful, specialized hardware.
Some of the main challenges of implementing AI workloads are:
- Resource allocation: Achieving low-latency, efficient AI workloads is a challenge for traditional processors like CPUs. Complex AI algorithms and high-volume data processing require AI-optimized hardware built for parallel processing. As AI systems grow in complexity and data density, scalability becomes a critical factor.
- Privacy and security: AI workloads depend on (and produce) vast amounts of data. This data can often contain anything from sensitive personal information to trade secrets to classified government intel. As the complexity of any system increases, so do potential security vulnerabilities. When dealing with such a large and varied amount of data, the importance of stringent security controls cannot be overstated.
- Maintenance: Over time, AI models occasionally need to be retrained and recalibrated to ensure accuracy. Regularly retraining AI models can be a cost- and labor-intensive process, and it must be carried out by qualified professionals.
- Ethical concerns: Operating AI systems with AI workloads raises a certain amount of new ethical considerations. Issues like managing algorithmic bias, providing model transparency, and ensuring accountability continue to spark debate. In the case of gen AI, which is often trained on original intellectual property, concerns around copyright and attribution pose interesting and important questions.
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