What's the difference between AI accelerators and GPUs?

An AI accelerator is any piece of hardware—including a graphics processing unit (GPU)—used to speed up machine learning (ML) and deep learning (DL) models (DL), natural language processing and other artificial intelligence (AI) operations.

However, the term AI accelerator is increasingly used to describe more specialized AI chips, such as neural processing units (NPUs) or tensor processing units (TPUs). While general-purpose GPUs—originally designed for rendering images and graphics—are very effective when used as AI accelerators, other types of purpose-built AI hardware might offer similar or better computational power with improved energy efficiency, greater throughput and other valuable optimizations for AI workloads.  

Standard central processing units (CPUs) operate under a linear framework, responding to requests one at a time, and they often struggle with high-performance data processing demands. GPUs are designed differently and excel at such requests.

Featuring multiple logic cores, GPUs break complicated problems into smaller pieces that can be solved concurrently, a methodology known as parallel processing. Originally developed by Nvidia in 2006, the CUDA API unlocked the impressive parallel processing power of the GPU. This allows programmers to use Nvidia GPUs for general-purpose processing in thousands of use cases, such as data center optimization, robotics, smartphone manufacturing, cryptocurrency mining and more. 

The GPU's impressive parallel processing capabilities have also proven extremely useful for AI tasks such as training large language models (LLMs) or neural networks. However, with increased demand comes increased power consumption, and high-performance GPUs are notoriously power-hungry and costly. 

Despite being well-suited for AI applications such as processing large datasets, GPUs aren’t specifically designed for use in AI models. As a graphics processor, the average GPU will allocate a certain amount of logic cores to graphics-related tasks. These tasks include video encoding and decoding, calculating color values and various rendering processes that are critical for tasks like video editing, 3D modeling and gaming. AI accelerator chips, however, are fine-tuned to handle only those tasks necessary for AI. 

Generally speaking, a GPU must be capable of processing a very large (but not massive) amount of data very quickly in order to render complex and rapid graphics smoothly in real-time. As such, GPUs prioritize low-latency operations to ensure constant and consistently high image quality.

While speed is also important in AI models, AI datasets are far larger than average GPU demands. Unlike GPUs, AI accelerators are designed to optimize for bandwidth, and as a result, they typically offer improved energy efficiency as well. 

Although GPUs are frequently used as AI accelerators, a GPU might not be the best option compared to a more specialized AI accelerator. The main differences between general-purpose GPUs and specialized AI chips are specialization, efficiency, accessibility and utility.

• Specialization: GPUs are designed for advanced parallel processing which can be repurposed for a variety of demanding tasks. However, they are specialized for video and graphics processing tasks and are used primarily for these purposes. 

• Efficiency: GPUs are known for requiring large amounts of electricity and are not regarded as resource-efficient solutions. High power consumption can negatively impact the scalability of any operation relying on a GPU or GPUs as the main type of processor. 

• Accessibility: GPUs are produced by many different major manufacturers, including AMD, Nvidia and Intel, and are widely available, although increased demand can impact cost. Having been in the market for many years, GPUs also enjoy a robust community of preexisting resources and are easily programmed through frameworks like CUDA. 

• Use cases: GPUs are the go-to processors for gaming, computer animation and video processing. Their parallel processing has also made them desirable for other applications requiring large-scale data processing, such as data centers, crypto-mining and some AI use cases.

• Specialization: AI accelerators are specialized for AI tasks and can be further specialized for specific types of AI applications. While AI accelerators can provide value within systems performing functions not related to AI, they are designed for and best applied to AI tasks.

• Efficiency: AI accelerators are often designed for very specific applications and are typically much more efficient than GPUs, providing similar parallel processing abilities while requiring much less energy resources. AI accelerators are able to shed excess features used by GPUs for graphics processing to optimize for AI tasks, such as the short, repetitive calculations and AI algorithms used in neural networks.

• Accessibility: AI accelerators are newer than GPUs and generally less accessible. Proprietary AI accelerators like the Google TPU (Tensor Processing Unit) might be less available to the general market. However, machine learning communities such as Pytorch and the open source TensorFlow are increasingly making AI accelerators more accessible through growing libraries of tools and resources. 

• Use cases: As a more specialized type of hardware, AI accelerator use cases are more narrow than GPUs, relegated to demanding AI tasks such as computer vision/image recognition, natural language processing and autonomous vehicles. However, as AI becomes more integrated into our daily lives, manufacturers have begun integrating AI accelerators like NPUs into more common consumer electronics, such as laptops and smartphones.  

For AI applications, a GPU can be a good general-use solution in the same way a pickup truck might be a happy medium between a sports car and an 18-wheeler. An 18-wheeler is slower than a sports car but can haul a lot more cargo. A pickup truck can haul some cargo, and is faster than an 18-wheeler, but is slower than a sports car.

The GPU is similar to a pickup truck—but depending on the priorities of the AI application, a more specialized AI chip, like a more specialized vehicle, might be preferable. 

Graphics processing units, sometimes called graphical processing units, were invented in the 1990s to ease the processing demand on CPUs as computing became less text-based and graphical operating systems and video games began to rise in popularity.

Since the invention of the modern computer in the early 1950s, the CPU has historically been responsible for the most critical computational tasks, including all program-necessary processing, logic and input/output (I/O) controls.

By the 1990s, video gaming and computer-aided design (CAD) demanded a more efficient way to convert data into images. This challenge prompted engineers to design the first GPUs with unique chip architecture capable of performing parallel processing.

Since 2007, when Nvidia introduced the GPU programming platform CUDA, GPU design has proliferated, with discovered applications across industries and well beyond graphics processing (although rendering graphics is still the most common application for most GPUs). 

Although there are hundreds of varieties of GPUs ranging in performance and efficiency, the vast majority fall into one of three major categories:

• Discrete: Discrete GPUs, or dGPUs, are separated from a system’s CPU. As distinct, individual pieces of hardware, dGPUs are often used in advanced applications, such as large-scale video editing or high-performance gaming. 

• Integrated: Integrated GPUs, or iGPUs, are built directly into system infrastructure and combined with the CPU. Integrated GPUs offer simplified infrastructure without compromising performance and are frequently used in laptops and handheld gaming consoles. 

• Virtual: Virtual GPUs offer the same functionality as other types of GPUs, without the hardware. A virtual GPU uses virtualization software to create a code-based GPU that is useful for cloud-based applications. As virtual GPUs do not require any dedicated hardware, they are simpler and cheaper to implement and maintain. 

While AI accelerator means any piece of hardware used to speed up artificial intelligence applications, an AI accelerator most commonly refers to specialized AI chips optimized for specific tasks associated with AI models.

Although they are considered highly specialized hardware, AI accelerators are built and used by legacy computing companies including IBM, Amazon Web Services (AWS) and Microsoft, as well as startups such as Cerebras. As AI matures and grows in popularity, AI accelerators and accompanying toolkits are becoming more common. 

Before the invention of the first dedicated AI accelerators, general-purpose GPUs were (and continue to be) frequently used in AI applications, specifically for their advanced parallel processing power. However, as AI research has advanced over the years, engineers have sought AI accelerator solutions offering improved power efficiency and niche AI optimizations. 

AI accelerators vary based on both performance and specialization, with some proprietary technology relegated to specific manufacturers exclusively. Some of the more prominent types of AI accelerators include the following:

• GPUs: As a general-purpose AI accelerator, GPUs are valued for their powerful parallelism. However, they suffer from high energy consumption and reduced scalability. 

• Field programmable gate arrays (FPGAs): FPGAs are a type of configurable processor that can be programmed and reprogrammed to suit specific application demands. These types of chips are highly valuable for prototyping as they can be customized and tweaked throughout the development process to meet emerging application requirements. 

• Application-specific integrated circuits (ASICs): ASICs are custom chips designed for specific tasks. Because ASICs are typically tailor-made for their unique function, they are usually highly optimized for both performance and power consumption. 

• Neural processing units (NPUs): NPU architecture mimics the neural pathways of the human brain and prioritizes data flow and memory hierarchy for better processing AI workloads in real-time.

• Tensor processing units (TPUs): Similar to NPUs, TPUs are a type of proprietary AI accelerator manufactured by Google and designed for a high volume of low-precision computations, such as the type of tensor operations used in matrix multiplications common to most AI models. While most AI accelerators are also capable of these types of calculations, TPUs are optimized for Google’s TensorFlow platform. 

While an off the shelf GPU does offer certain advantages (for example, availability, accessibility) more specialized AI accelerators typically outperform older technology in three key areas: speed, efficiency and design.

Modern AI accelerators, even GPUs, are vastly faster than CPUs when it comes to low-latency, large-scale data processing. For critical applications such as autonomous vehicle systems, speed becomes critically important. GPUs are better than CPUs, but ASICs designed for specific applications like the computer vision used in self-driving cars are even faster. 

AI accelerators designed for specific tasks might be anywhere from 100 to 1,000 times more energy efficient than power-hungry GPUs. Improved efficiency can lead to dramatically reduced operational expenses and more importantly, far less environmental impact. 

AI accelerators employ a type of chip architecture known as heterogeneous design, which allows for multiple processors to support separate tasks and increases compute performance through highly advanced parallel processing. 

As GPUs are considered to be AI accelerators themselves, their use cases overlap frequently with more specialized AI hardware. In time, we might see GPUs take a backseat in AI applications. 

Versatile GPUs are still widely used in both AI and other types of applications, and this will undoubtedly continue. GPUs are used for a range of applications requiring advanced parallelism, including the following:

• Artificial intelligence, machine learning and deep learning: Although newer varieties of AI accelerators might one day replace GPUs in artificial intelligence applications, GPUs will likely remain highly valuable as co-processors within AI systems. Currently, GPUs power many leading AI applications, such as IBM’s cloud-native AI supercomputer Vela, that require high speeds to train on larger and larger datasets. GPUs continue to provide value for machine learning and deep learning applications—such as training neural networks—as well. 

• Blockchain: Zero-trust blockchain technology is used to record transactions in virtual ledgers, and it is the basis for popular cryptocurrencies such as Bitcoin. The advanced processing power of GPUs remains highly valuable within blockchain applications, especially when concerning “proof-of-work” operations that validate ledger transactions. 

• Graphics: Applications demanding high-performance graphics rendering depend on GPUs. GPUs are an integral part of major industries including gaming, video editing and content creation. GPUs also play an important part in visualization and simulation tasks, such as 3D modeling, weather forecasting, and medical, seismic and geophysical imaging. 

As AI technology matures, specialized hardware is becoming more and more prevalent. Incorporating the parallel processing power of GPUs while discarding unnecessary features, ASIC AI accelerators are being used in a growing range of applications, including the following:

• Autonomous vehicles: Capable of real-time data processing, specialized AI accelerators have become a critical component of self-driving autonomous vehicle systems where milliseconds matter most. AI accelerators capture and process data from input sensors including cameras and LiDAR, allowing for autonomous vehicles to interpret and react to the world around them. 

• Edge computing and edge AI: Edge computing and edge AI refer to infrastructure frameworks that bring applications and compute power closer to cloud-based data sources such as Internet of Things (IoT) devices, facilitating faster and more secure connections. Cloud-based AI can pose security concerns, and AI accelerators help localize AI models to reduce the opportunity for compromising sensitive data. 

• Generative AI: Generative AI models, such as LLMs, depend on AI accelerators for natural language processing, helping the AI model understand casual conversational commands and produce easily understood responses in applications like chatbots.