What is an ASIC?

A CGI image of hardware chips sat on top of a glassy monitor showing a red, rising price line, to represent rising hardware costs. — (Image credit: Getty Images)

At the heart of all of our technology in the 21^st century – from smartphones to computers to servers to manufacturing machines – lies the humble microchip.

These tiny components of integrated circuits could be described as the brain through which everything happens, whether that’s processing data or storing information.

Over the decades, the main goal when designing such chips has always been to make them smaller, more powerful, and faster than ever – and there are many different types created for all sorts of different devices and roles.

For example, logic chips such as central processing units (CUPs), graphics processing units (GPUs), and neural processing units (NPUs) are the key to making something work or happen. Separately, there are also memory chips that store data, for instance flash or random access memory (RAM).

What on earth then is an ASIC?

The application-specific integrated circuit (ASIC) is a microchip that’s custom-designed and optimised to do a single designated task absolutely perfectly.

This enables it to achieve the sort of speed, power, and efficiency that isn’t possible when a microchip is designed to handle many tasks. Peter Groos, senior AE Group director, at Cadence Design Systems, explains: “They’re optimized for particular, predictable workloads, enabling higher performance and greater energy efficiency when that task is well-defined.”

Who makes ASICs?

ASICs are designed by companies such as Broadcom, Nvidia,Qualcomm, and many others and manufactured by foundries such as TSMC and Samsung.

“These companies collaborate across the value chain, with designers creating chip architectures and foundries fabricating them at advanced process nodes,” Groos adds.

What types of ASICs are in use right now?

The AWS Trainium family of chips from Amazon is an example of an ASIC. These have been specifically designed for training and running AI models and platforms.

Other examples of where an ASIC can be used are in:

5G network signal processing and packet routing in telecommunications
Advanced driver assistance systems (ADAS) in motor vehicles
Bitcoin Mining or for the creation of other cryptocurrencies
Face detection or voice recognition in smartphones and other smart devices
Hearing aids or medical imaging within the healthcare sector

What else is interesting about an ASIC?

ASICs are built on the idea of stripping away everything unnecessary to ensure they are fully focused on executing the single task at hand, however complex it is. But they can also be built to be SoCs, with everything in the one place that’s needed for the one job.

ASICs will often be found in modern data centers and industries where compact and energy-efficient computing is critical. According to BAE Systems, you will also find special radiation-hardened ASICs in nuclear power plants, spacecraft, and inside satellites in orbit.

Where do ASICs sit within AI?

“As AI systems scale, ASICs are becoming increasingly important for efficiency, particularly in data centres and edge deployments,” says Gross.

“The role of ASICs in the AI ecosystem is based on their ability to naturally complement the GPUs that are so critical to AI infrastructure.”

This allows ASICs to play a key role in optimising network traffic and workload distribution within systems, Gross further explains, adding: “ASICs are used because they deliver superior speed and power efficiency compared to general-purpose chips when handling predictable, repetitive workloads.

“In AI, this makes them particularly valuable for tasks where performance-per-watt and cost efficiency are critical, such as large-scale inference and data movement.”

What else is important about ASICs?

There are three different types of ASIC, each with its own clear role: full custom ASIC, semi-custom ASIC, and programmable ASIC.

As the names suggest, the first is built from the ground up, the second will use pre-designed parts, and the third can be programmed for a defined use once it is manufactured.

With such a tiny physical footprint, each ASIC has no wasted action or space onboard; everything serves a defined purpose.

Google's tensor processing units (TPUs) – a type of ASIC – are said by the company to push computing performance by up to 15-30x, while their energy efficiency can rise from 30-80x versus a general-purpose processor of the same level.

Why have ASICs become so important?

Amazon’s entry into ASICs with AWS Trainium shows how vital they are to the future of computing, technology, and AI. This is a battle for supremacy in a multi-billion-dollar race, and the potential efficiencies and cost-savings ASICs can go on to deliver are enticing other big players to join in. Google and Meta, for example, are also investing in their own ASICs.

ASICs’ role in processing data at the edge also makes them vital for smart cities, smart appliances, and autonomous vehicles.

What is the future for ASICs?

A lot of investment is being pumped into ASIC design and manufacture right now. For example, in April 2026 NVIDIA invested $2 billion in Marvell, a company that counts Amazon, Microsoft, Google, and Meta among its customers.

This comes as the entire semiconductor industry is expected to reach $1.6 trillion in revenue by 2030, up from $775 billion in 2024, according to McKinsey.

Broadcom is another benefiting from this surge in interest. Its Custom ASICs are provided to the likes of Google, Meta, OpenAI, and Apple, and Q2 FY2026 revenue for AI semiconductors soared 143% to $10.80 billion year over year. CEO Hock Tan projects AI revenue exceeding $100 billion in 2027.

Amazon, which makes the AWS Inferentia chip family (another custom-made ASIC), is also buoyant about the future of its chips business. Amazon CEO Andy Jassy said in late April 2026 that this part of the company saw 40% quarter-to-quarter growth in Q1 2026.

He also revealed that while its AWS Trainium4 chips won’t roll off the production line until 2027, much of its available inventory is now already reserved. “As best as we can tell, our custom silicon business is now one of the top three data center chip businesses in the world – and the speed at which we’ve gotten here is extraordinary,” he says.

Engineer and tech entrepreneur Marco Riedesser, however, sees another change coming over the horizon, pointing to first-generation hardwired chips from companies like Taalas. These chips put the AI model into the silicon.

“We are moving from a world where we run AI on computers to a world where the chip is the AI,” he explains.

“By removing the need to move data back and forth from memory, ‘hardwired AI’ can run medium-sized models at speeds 10x–50x faster than traditional GPUs.”

Riedesser describes how instead of loading a model like Llama 3 into a chip's memory, you instead “etch the model’s logic” into the metal of the chip itself.

He predicts that “while AWS and Nvidia are building chips to run software, companies like Taalas are fundamentally changing the game”.

Jonathan Weinberg is a freelance journalist and writer who specialises in technology and business, with a particular interest in the social and economic impact on the future of work and wider society. His passion is for telling stories that show how technology and digital improves our lives for the better, while keeping one eye on the emerging security and privacy dangers. A former national newspaper technology, gadgets and gaming editor for a decade, Jonathan has been bylined in national, consumer and trade publications across print and online, in the UK and the US.