What Is AI Hardware And How Does It Work?

AI hardware and AI software are interdependent components that rely on one another to create an optimally functioning artificial intelligence model.

AI software can only be as powerful as the hardware allows it to, and software must be designed to take total advantage of the hardware it is integrated with. As AI hardware advances, it can lead to improved performance and processing.

As the boundaries of AI capabilities are continually being tested and the technology improves, innovation in AI hardware is at an all time high.


What Is AI Hardware?


AI hardware is the physical device that is designed to perform artificial intelligence tasks. AI hardware is more efficient than general use hardware for completing these tasks.

These systems are necessary for AI algorithms to run efficiently, ensuring that AI applications can execute advanced computations with speed and accuracy.


How Does AI Hardware Work?


AI hardware can be classified in two main categories, known as AI training hardware and AI inference hardware.

AI training hardware is used to train AI models. This hardware required high computational power, memory bandwidth, and parallelism to handle the large amounts of data and complex calculations that are involved in training AI models. It uses large datasets with complex algorithms and mathematical operations to achieve this.

AI training hardware is typically made of graphics processing units, tensor processing units, or field-programmable gate arrays.

AI inference hardware allows AI models to make predictions and decisions based on new data. This hardware requires low latency, a power supply and other costs to handle the real-time demands of AI applications.

AI inference hardware is typically made of application-specific integrated circuits, neural processing units, or intelligent processing units.


Why Is AI Hardware Important?


AI hardware is so important because it directly influences the efficiency and feasibility of AI. AI hardware must be powerful enough to handle the increasing integration and demand for AI powered systems.

Improving AI hardware encompasses more than increasing raw computing power – it also involves building systems that better mimic the ways our brain works to process information. Improved AI hardware can enable faster processing speeds, reduce energy consumption and allow AI algorithms to perform more sophisticated tasks.

Creating AI models that are more efficient will be necessary to create a future where it can be seamlessly integrated in our devices.

AI Hardware Startups


AI Hardware startups play an important role in advancing AI technology, as improved hardware translates into improved systems. These startups are transforming the field of artificial intelligence, enabling researchers and entire industries to do more with less time and less resources.


Axelera AI




Axelera AI develops a hardware and software platform that accelerates computer vision on edge devices.

Its in-memory computing and RISC-V controlled dataflow technology allows its platform to deliver top performance and usability with much less resources, including finances and energy, compared to other solutions.






Graphcore develops IPUs that have the potential to transform various industries, from drug discovery and disaster recovery to decarbonisation.

Its IPU is a new processor designed for AI compute. It allows AI researchers to embark on tasks that would otherwise not have been possible using current technologies, to further advance machine intelligence.






Enfabrica has developed the industry’s first multi-GPU SuperNIC chip, enabled by Enfabrica’s patented Accelerated Compute Fabric architecture.

Enfabrica also built half of the technologies that more than half of today’s global data centre traffic runs on.






Cambricon is a Chinese tech company headquartered in Beijing that develops and distributes integrated circuits, electronic chip products and platform based basic system software.

Cambricon also builds core processor chips designed for artificial intelligence applications.






Cerebras developed the CS-3 chip, capable of answering questions in minutes or hours that would typically take days, weeks, or longer.

The CS-3 equates the performance of a room full of servers, all in a single unit the size of a dorm room mini-fridge. This cluster-scale compute, available in a single device, enables researchers to do more with less resources.