What Is AI Hardware?

AI hardware encompasses the general purpose and specialized computer parts and components used to facilitate artificial intelligence tasks. They enable quick processing of large datasets with efficiency and scale. Some examples of AI hardware components include processors, AI accelerators, and specialized memory units.

The type of AI hardware you use will depend on your use case, the scale and complexity of the AI workload being processed, and how quickly data needs to be analyzed. For example, AI used in financial services for fraud detection needs to process millions of data points per day in near-real time. AI-enabled sensors used in autonomous vehicles process smaller workloads at the edge where data is collected in near-real time for human safety. AI chatbots used to provide customer service answers on retail websites have fewer parameters to analyze and have less stringent response time requirements.

Role of Hardware in AI

The role of hardware for artificial intelligence is both fundamental and multifaceted. Different components affect different aspects of AI computation, meaning the kind of AI hardware your system uses will significantly influence its ability to perform certain AI tasks.

For example, processor speed directly impacts how quickly AI models perform calculations. Memory and storage affect how much data can be handled simultaneously and how quickly that data can be accessed. Your system’s overall design, including cooling and power delivery, affects sustained performance over time and needs to be optimized to handle continuous AI workloads.

Benefits of AI Hardware

There are many kinds of AI hardware components, and each component comes with a different set of benefits and drawbacks. Depending on what AI tasks you are demanding of your system, certain components may make more sense to either include or omit.

AI Processors and Accelerators

AI processors provide the computational power needed to complete AI tasks, while AI accelerators, both integrated and discrete, are used to unlock advanced AI performance.

It’s important to note that common descriptors and standardized language have not yet emerged for many of these technologies and include many vendor-specific terms.

AI hardware types you should be aware of include:

• CPU: A central processing unit plays a foundational role in AI systems, meeting the general-purpose needs of AI applications like data preprocessing, model orchestration, and control flow.
  
  CPUs provide a high degree of flexibility when dealing with smaller AI models, making them well suited for a wide range of AI workloads and use cases that require high single-threaded performance, low latency, and complex sequential logic, such as real-time systems and less-complex natural language processing tasks.
  
  CPUs may also be available with integrated accelerators—accelerator engines that help optimize AI performance.
   

• GPU: Originally designed for rendering graphics, a graphics processing unit is a type of discrete hardware AI accelerator that excels at performing many calculations at the same time. It can process large datasets much more quickly and efficiently than CPUs, significantly speeding up the training of AI models and making GPUs ideal for deep learning and computer vision.

• TPU: A tensor processing unit is another type of AI accelerator designed specifically for AI workloads. It is made for handling large-scale learning tasks and offers high performance and energy efficiency. Its architecture allows it to perform rapid matrix multiplications and convolutions fundamental to many AI algorithms. Compared to CPUs, TPUs significantly speed up computations, enabling faster training of complex models and more efficient usage of AI services in cloud environments.

• NPU: A neural processing unit is a specialized, AI-accelerating hardware component for computations in neural networks and machine learning models that are designed to process data in a way similar to the human brain. NPUs are optimized for common artificial intelligence operations and machine learning tasks, like matrix multiplication, convolutions, and activation functions. NPUs are extremely efficient options for AI and offer fast speeds and high bandwidth, which make them ideal for integration into fast-moving workflows.

• FPGAs: Field-programmable gate arrays are very versatile AI hardware solutions. They are reconfigurable and can be programmed to fit the needs of various AI tasks, enabling updates and modifications without hardware replacement. They are used in parallel computing architectures to fuel AI performance and are especially suited for real-time processing, computer vision tasks, and neural network inference at the edge, where devices and applications need to be adaptable and high performing.

Memory

Memory provides the critical infrastructure needed to perform AI tasks. It ensures that data and instructions are readily available for your processing units, allowing for the rapid and efficient execution of AI algorithms and reducing bottlenecks in AI operations. The capacity and speed of memory directly impact its ability to handle large datasets and complex models, both crucial components of AI performance.

While all computing systems come with some form of memory capacity, you can optimize your system’s capacity for AI processing through different kinds of hardware. Each memory type has its place in AI systems, often used in conjunction with one another to balance speed, capacity, and cost based on your AI performance demands:

• Random access memory (RAM): RAM is the primary memory component for AI systems, providing fast, temporary storage for active data and model parameters. RAM is quick to both read and write data, making it ideal for handling constant data computations. However, its volatility and capacity can be a limiting factor for larger-scale AI operations.
• Video RAM (VRAM): VRAM is a specialized memory component used in GPUs. While created to handle graphical data, its ability to perform parallel operations, which increase efficiency in some complex AI tasks, makes it useful for training neural networks and deep learning models. VRAM is usually more costly and has less capacity than standard RAM.
• High bandwidth memory (HBM): HBM was designed for high performance computing, offering very high bandwidth and allowing for much faster data transfer between processing units. It is ideal for training large neural networks or running complex simulations through GPUs and AI accelerators. HBM is also more expensive and has less capacity than other forms of memory.
• Non-volatile memory: Non-volatile memory, such as solid-state drives (SSDs) and hard disk drives (HDDs), offers long-term storage for AI data. Its strength is its ability to retain data without maintenance or power, but it is significantly slower than RAM or VRAM. Non-volatile memory’s primary use in AI systems is for data persistence rather than active processing.

AI Hardware Solutions

The type of AI hardware you select for your system will depend on where you are running your AI applications, the size of the dataset, and the required processing speed.

Client Computing

Client computing processors are typically found in personal computers and mobile devices. While standard PCs include CPUs and GPUs, the capabilities of those components have not been traditionally designed to meet the processing needs of AI applications and have required the use of the cloud for running AI models and analyzing AI datasets. However, with the exponential adoption of AI, new AI PCs with specialized hardware have been introduced to the market with an integrated NPU that makes it possible to run AI workloads efficiently on the device itself. This helps deliver faster processing and responsiveness—even without an internet connection—and helps to reduce costs and data security risks, as data isn’t being sent to and from the cloud. Recent innovations are also enabling more AI workloads to run on CPU-only architectures.

Edge

Edge computing applications that collect, process, store, and act on data closer to where it is generated require faster data analysis and near-real-time responsiveness. Insights generated at the edge are used for industrial, medical, test and measurement, aerospace, defense, and automotive applications. They can have immediate consequences for human safety—such as in autonomous driving scenarios—impact industrial operations—such as when used with IoT devices in manufacturing automation—or enable better experiences when applied to retail, healthcare, and telecommunications use cases. At the edge, CPUs and FPGAs offer the flexibility and efficiency benefits that can help extend AI capabilities to more places.

Data Center

In both on-premises and cloud data center environments, a combination of CPUs, GPUs, and specialized AI accelerators are being used to handle large-scale AI workloads in centralized server environments. CPUs are suited to a wide number of workloads and applications, especially those where latency or per-core performance are critical concerns, while GPUs and other specialized AI accelerators can be used alongside CPUs to meet the elevated computational demands of extremely complex AI workloads.