Every search you do on a chatbot, biometric facial scan to unlock your phone, or recommendation for a specific product is the result of an AI workload. Because artificial intelligence (AI) plays an increasingly important role in a wide range of industries, it is vital that executives, developers, and IT leaders understand what these workloads are, what the differences are between various AI workloads and traditional computer workloads, and the requirements for supporting them. In this blog we explore what exactly is AI workload and other concepts.
AI workload definition
An AI workload is a type of computer workload that requires training, operating, or fine-tuning artificial intelligence or machine learning (ML) models. Unlike traditional computer workloads, which are deterministic, AI workloads are heuristic and involve pattern recognition and statistical inference.
Furthermore, because these tasks are more complex and intensive, AI workloads often require specialized computer hardware, such as GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units).
AI workloads can be categorized based on what stage they are in, such as the following:
- The training workloads required to train or finetune ML models to process information and generate outputs.
- The inference workloads required to use already-trained models to process information and generate new outputs.
Both types of workloads often require substantial computational power. While training workloads are often time-consuming and require substantial resources and data, inference workloads often require large-scale implementations to accommodate millions or even billions of queries every day. Overall, both stages involve unique requirements, including specialized hardware, memory, storage, networking, and more. Now let’s take a closer look at the different categories of AI workloads.
What are the different types of AI workloads?
There are several categories of AI workloads, including the following:
1. Machine learning (ML) workloads
Tasks and sets of tasks that involve using or designing ML algorithms to process or analyze information. These algorithms can range from conventional regression or decision trees to more complex clustering algorithms, and they find use in a variety of applications, such as financial fraud detection, demand forecasting, and customer segmentation.
2. Deep learning workloads
Tasks and sets of tasks that involve using deep learning algorithms to process or analyze information. These algorithms utilize multiple layers and interconnections to process information, and they form the foundation for most modern and advanced AI systems. This category of AI workloads often requires large-scale parallel processing power provided by GPUs.
If you’d like to learn more about how GPUs work, read our guide on What Is a GPU?
3. Natural language processing (NLP) workloads
Tasks and sets of tasks that involve using NLP algorithms to process or analyze natural human languages. NLP algorithms are used in a variety of applications, including chatbots, automatic translation, and large language models (LLMs), and they are some of the most resource-intensive workloads currently in existence.
4. Computer vision workloads
Tasks and sets of tasks that involve using computer vision algorithms to process or analyze imagery or video. These algorithms find use in a variety of applications, including facial recognition, medical image diagnosis, and autonomous driving systems.
5. Generative AI workloads
Tasks and sets of tasks that involve using generative algorithms to produce text, imagery, or other types of data. These algorithms analyze existing data to identify patterns and then create new data based on this information.
Generative algorithms currently find use in a variety of applications, and they are among the most resource-intensive workloads that currently exist. Moreover, many of these workloads require large-scale computational networks for training, which has spurred interest in large-scale cloud AI infrastructure worldwide.
Why do AI workloads have different infrastructure needs than traditional computer workloads?
Traditional central processing units (CPUs) are generally adequate for most conventional computer workloads, but they are not optimized for pattern recognition and statistical inference, which are integral to most AI workloads. For example, modern graphical processing units (GPUs) enable significantly faster parallel processing than CPUs do, which helps AI workloads perform complex calculations faster. Additionally, there are several other infrastructure-related challenges involved in supporting AI workloads, including the following:
Computational power
Most AI workloads benefit significantly from using GPUs or TPUs to perform heavy calculations. Moreover, TPUs or specialized application-specific integrated circuits (ASICs) provide substantial benefits for certain deep learning workloads. If you’re unsure which processor is better suited for different AI tasks, read our guide on TPUs vs GPUs to understand their key differences and use cases.
Memory
AI workloads require substantially larger amounts of memory than traditional workloads, especially during the training stage.
Storage
Most AI workloads require faster storage solutions to ensure rapid access to data.
Networking
Some workloads which are related to distributed or clustered training, require faster networking infrastructure to ensure rapid access to data and efficient model synchronization.
Scalability
The ability to scale AI infrastructure up and down as needed is often integral to ensuring that workloads are cost-effective.
Do organizations typically host AI workloads in the cloud, on-premises, or do they use a hybrid approach?
The majority of organizations have the option to select from the following approaches:
On-premises
This approach typically requires organizations to build or purchase large-scale infrastructure to support their AI-related activity. They are most suitable for organizations with considerable financial resources and/or legal or regulatory obligations that prevent them from utilizing cloud solutions.
Cloud
Most organizations that opt for this approach rely on cloud providers to supply the infrastructure required to support their AI-related activity. This option is more convenient for organizations that do not possess the necessary financial and technical resources to support on-premises infrastructure.
Hybrid
As the name suggests, this option combines the previous two options. Because these solutions are flexible, they are typically the most desirable option for most organizations. However, they also tend to be the most expensive.
What are some key trends related to AI workloads?
The optimization of AI workloads is currently a major priority for the majority of organizations, as there are several cost-saving measures that can significantly affect their bottom line. For example, many companies are currently focusing on accelerating or optimizing these workloads to make them more cost-effective.
Moreover, AI workload optimization is also integral to workload orchestration, which ensures that GPUs are used efficiently. There are numerous techniques that can be employed to optimize workloads, such as model quantization and pruning. Overall, understanding the requirements for different AI workloads is essential in ensuring they are supported by appropriate infrastructure in an optimal manner.
Conclusion
An AI workload is a type of computer workload that requires training, operating, or fine-tuning artificial intelligence or machine learning (ML) models. Understanding the fundamental characteristics and differences between various AI workloads and traditional computer workloads is essential in selecting the most appropriate infrastructure and optimizing these workloads. Organizations should be aware of the unique requirements for supporting traditional computer workloads and AI workloads, particularly the specialized infrastructure that is often necessary for the latter. Overall, the importance of AI workloads keeps growing, and organizations must ensure that they have the necessary infrastructure to support them to maximize efficiency and reap substantial financial benefits.
FAQs
What is the difference between an AI workload and a traditional computer workload?
Traditional computer workloads are deterministic, whereas AI workloads are heuristic and involve pattern recognition and statistical inference. Furthermore, AI workloads often require specialized computer hardware, such as GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units).
Why do AI workloads need GPUs instead of CPUs?
GPUs provide much greater parallel processing power compared to CPUs, which is crucial for the performance of AI workloads.
Should my business use cloud or on-premises infrastructure for AI workloads?
It depends on your needs and priorities. Cloud infrastructure is more flexible and often less expensive, whereas on-premises infrastructure provides much greater control and security.
What industries leverage AI workloads the most?
Healthcare, finance, retail, manufacturing, and the technology sector are among the largest users of AI workloads. These industries leverage a wide range of AI-driven applications and solutions, including recommendation systems, fraud detection systems, chatbots, and large-scale data analysis solutions.
How can companies reduce costs associated with AI workloads?
Optimization techniques, such as model quantization, can help reduce the costs associated with AI workloads.
