AI Chips Explained: GPU vs TPU vs NPU vs Custom Silicon (2026)

AI chips are specialised processors built to run artificial intelligence workloads — mainly the massive parallel maths behind neural networks — far faster and more efficiently than an ordinary CPU. The four families you will hear about are the GPU (graphics processing unit, the workhorse of AI training), the TPU (Google’s tensor processing unit), the NPU (neural processing unit found in phones and laptops), and custom silicon or ASICs (chips designed for one company’s exact needs).

Why AI needs special chips

For decades, the CPU (central processing unit) was the brain of every computer. CPUs are brilliant generalists: they handle a few tasks at a time but switch between them with great flexibility, running everything from your operating system to a spreadsheet. The problem is that modern artificial intelligence does not look like a spreadsheet. It looks like a wall of multiplication.

At their core, neural networks — the technology behind tools like ChatGPT, Google Gemini and image generators — are built on a single mathematical operation repeated billions of times: matrix multiplication. To process one sentence or one image, the system multiplies and adds enormous grids of numbers. A general-purpose CPU does this one small chunk at a time. That is like asking one very smart accountant to add up a stadium full of numbers by hand.

AI chips take a different approach: massive parallelism. Instead of one fast accountant, imagine thousands of simpler calculators all working at once, each handling a tiny piece of the sum. That is why a graphics card with thousands of cores can train a model hundreds of times faster than a CPU. The same maths that draws millions of pixels in a video game turns out to be exactly what a neural network needs.

What “chips for AI” actually optimise for

Three levers separate an AI accelerator from an ordinary processor:

• Parallel cores: thousands of arithmetic units that crunch numbers simultaneously.
• Lower-precision maths: AI rarely needs 64-bit accuracy. Chips use 16-bit, 8-bit or even 4-bit numbers, which means more calculations per watt and per rupee.
• Memory bandwidth: feeding those cores with data fast enough is often the real bottleneck, which is why high-speed memory sits right next to the chip.

How an AI chip actually works

Every neural network is a stack of layers. Data enters, gets multiplied by a grid of learned weights, gets a simple non-linear function applied, and passes to the next layer. Repeat this dozens or hundreds of times and you get a prediction — the next word, the label on an image, the route for a self-driving car.

An AI chip is engineered so that the multiply-and-add step (engineers call it a “multiply-accumulate” or MAC operation) happens in dedicated hardware blocks, sometimes arranged in a grid called a systolic array, where numbers flow through rows and columns of multipliers like water through pipes. Google’s TPU is the most famous example of this design. The result is that the chip spends its energy on useful maths rather than on shuffling instructions around.

GPU vs TPU vs NPU vs ASIC: the four families

People say “AI chips” as if they are one thing. In reality there are several families, each tuned for a different place in the AI stack — from a giant data centre in another country to the phone in your pocket.

GPU — the workhorse of AI

The GPU was invented to render graphics, but its thousands of cores made it perfect for the parallel maths of deep learning. Today the GPU is the default chip for training large models, and Nvidia’s data-centre GPUs dominate this market. A GPU is flexible: it can train, it can run predictions, and it works with almost every AI software framework. That flexibility is why a startup in Bengaluru and a hyperscaler in California both reach for the same kind of chip. The trade-off is cost and power draw — top data-centre GPUs are expensive and hungry for electricity.

TPU — Google’s tensor processing unit

A TPU is a chip Google designed specifically for neural networks (a “tensor” is just the multi-dimensional grid of numbers that flows through a model). Because it does one job, a TPU can be more efficient than a general GPU for that job. TPUs power many of Google’s own services and are rented out through Google Cloud. They are a leading example of a company building its own silicon instead of buying off the shelf.

NPU — the AI engine in your phone and laptop

An NPU (neural processing unit) is a small, power-sipping AI accelerator built into the main chip of phones, laptops and smart devices. This is “edge AI” — running the model on the device itself rather than in the cloud. Your phone’s NPU powers features like on-device photo enhancement, live translation, voice assistants and face unlock, all without sending data to a server. Qualcomm’s Snapdragon, Apple’s Neural Engine, MediaTek’s Dimensity and the AI engines in “AI PC” laptops (including AMD’s Ryzen AI and Intel’s chips) all use NPUs. For Indian consumers, the NPU is the AI chip you most directly own — it is the reason a mid-range phone can edit photos or transcribe speech offline.

ASIC / custom silicon — chips built for one purpose

An ASIC (application-specific integrated circuit) is a chip hard-wired for a single workload. A TPU is technically a kind of AI ASIC. Increasingly, the biggest technology companies design their own custom silicon to cut costs and reduce dependence on outside suppliers — examples include Amazon’s Trainium and Inferentia, Microsoft’s Maia and Meta’s in-house accelerators, several of them designed with help from Broadcom or Marvell. The upside is peak efficiency and control; the downside is that an ASIC is inflexible and enormously expensive to design, so it only makes sense at huge scale.

Side-by-side comparison table

Here is how the four families stack up on the things that matter — what they are best at, how flexible they are, and where you encounter them.

Training vs inference: two very different jobs

To understand why so many different chips exist, you have to separate the two phases of an AI model’s life. They make almost opposite demands on hardware.

Training: teaching the model

Training is the one-time (but gigantic) process of building a model. The system is shown enormous amounts of data and adjusts billions of internal weights until it gets good at the task. This is brutally compute-heavy, runs for weeks across thousands of chips wired together, and consumes a lot of power and money. Training is where data-centre GPUs and TPUs earn their keep, and where memory bandwidth and the network linking the chips matter as much as raw speed.

Inference: using the model

Inference is what happens every time you actually use the model — you type a prompt, it answers. Each individual request is far lighter than training, but it happens billions of times a day, so efficiency and cost-per-query dominate. Inference can run on cheaper data-centre chips, on dedicated inference accelerators, or — for small models — on the NPU in your own phone. As AI moves from research into everyday products, the industry’s attention is shifting from “who can train the biggest model” to “who can serve inference most cheaply.”

Who makes AI chips: Nvidia, AMD, Broadcom & more

The AI chip industry has a clear leader, a strong challenger, and a fast-growing group of cloud giants designing their own silicon. Understanding the players helps explain the headlines about export controls, valuations and supply shortages.

Nvidia — the runaway leader

Nvidia sits at the centre of the AI boom. Its data-centre GPUs (the Hopper generation and the newer Blackwell architecture) are the chips most AI labs train on, and its CUDA software platform has become the default toolset, creating a powerful lock-in. Nvidia’s dominance turned it into one of the most valuable companies on Earth, crossing into multi-trillion-dollar territory. Its products are also at the heart of US export rules that restrict sales of the most advanced AI chips to China.

AMD — the main challenger

AMD competes with its Instinct line of data-centre accelerators (the MI300 series and successors) and is the credible number two in GPUs. AMD also leads the “AI PC” push on the consumer side with its Ryzen AI chips, which pair a CPU, GPU and NPU on one package for laptops.

Broadcom, Marvell and the custom-silicon wave

Not every AI chip carries a famous logo. Broadcom and Marvell are the quiet giants that help cloud companies design custom ASICs and build the high-speed networking that ties data-centre chips together. When Google, Meta, Amazon or Microsoft build an in-house accelerator, a company like Broadcom is often the partner doing the heavy engineering. This is why Broadcom has become one of the most important names in AI hardware even though consumers never buy its chips directly.

Intel, the cloud hyperscalers and the edge players

Intel is fighting to stay relevant in AI with its Gaudi accelerators and AI-enabled PC processors. Meanwhile the cloud giants — Amazon (Trainium/Inferentia), Google (TPU), Microsoft (Maia) and Meta — increasingly design their own chips to reduce cost and dependence on Nvidia. On the edge, Qualcomm, Apple and MediaTek dominate the NPUs inside phones, while Arm supplies the underlying chip designs that almost every mobile processor is built on. A wave of specialist startups (such as Cerebras, with its wafer-scale chip, and Groq) is also chasing faster, cheaper inference.

The supply chain: TSMC, HBM memory & the bottlenecks

Designing an AI chip is only half the battle. Someone has to actually manufacture it, and here the world depends on a remarkably small number of suppliers. This concentration is why “the chip supply chain” is now a topic for prime ministers, not just engineers.

TSMC: the factory the world runs on

Most advanced AI chips — including Nvidia’s and Apple’s — are physically manufactured by TSMC (Taiwan Semiconductor Manufacturing Company). TSMC is a “foundry”: it does not design chips, it fabricates them for other companies on the world’s most advanced production lines. Because cutting-edge fabrication is concentrated in Taiwan, the global AI industry has a single critical dependency, which is at the heart of US–China tensions and the worldwide rush to build more fabs.

HBM: the memory bottleneck

An AI chip is only as fast as the data you can feed it. That data lives in HBM (high-bandwidth memory) — specialised memory stacked right next to the processor to move enormous amounts of data per second. HBM is made by a handful of firms, chiefly SK Hynix, Samsung and Micron, and demand for it has at times outstripped supply. In many AI systems, the scarce, expensive HBM — not the processor itself — is the real limiting factor.

ASML and the rest of the chain

Go one step further back and you reach ASML of the Netherlands, the only company that makes the extreme-ultraviolet (EUV) lithography machines needed to print the smallest transistors. So the AI hardware that powers a chatbot rests on a chain that runs roughly: ASML’s machines → TSMC’s fabs → HBM from Korea → chip designs from Nvidia or a cloud giant → assembled systems in a data centre. A shock at any link — a natural disaster, an export ban, a shortage — ripples through the entire industry.

India’s semiconductor push

India does not yet manufacture leading-edge AI chips, but it has launched one of the most ambitious efforts of any emerging economy to enter the semiconductor world — and it already holds a powerful card in chip design.

The India Semiconductor Mission and the fabs

Through the India Semiconductor Mission and a large incentive programme, the government is offering financial support to attract chip fabrication (“fab”) and assembly-and-test plants. Several projects have been approved, the most prominent being a fab in Dholera, Gujarat, backed by the Tata Group in partnership with Taiwan’s PSMC, along with assembly and testing facilities (often called OSAT/ATMP plants) by players including Tata, the CG Power–Renesas grouping, and Micron’s facility in Sanand, Gujarat. The near-term aim is not to beat TSMC at the cutting edge, but to build mature-node manufacturing, packaging and a skilled workforce.

India’s real strength: design talent

India’s most immediate advantage is people. A very large share of the world’s semiconductor design engineering happens in India, and global chip companies — Nvidia, AMD, Intel, Qualcomm, Micron, Broadcom and others — run major design and R&D centres in cities like Bengaluru, Hyderabad and Noida. The country is also investing in indigenous and open RISC-V chip designs and in compute infrastructure for AI through national AI programmes that aim to make subsidised GPU capacity available to startups and researchers.

What it means for Indian businesses and students

For founders, cheaper access to AI compute — whether via global cloud providers or India-backed initiatives — lowers the barrier to building AI products. For students and engineers, semiconductor design, chip verification, embedded AI and hardware roles are becoming serious long-term career paths, not niche ones. The honest picture in 2026 is that India is early in the journey: strong on design and software, building fast on manufacturing, and still dependent on imports for the most advanced AI chips.

How to think about choosing a chip

Most readers will never buy a data-centre GPU, but the same logic applies whether you are a startup picking cloud instances or a buyer choosing a laptop. Match the chip to the job.

• Training a large model? You want data-centre GPUs or TPUs, almost always rented from a cloud provider rather than bought.
• Running inference for an app? Optimise for cost-per-query — cheaper GPUs, dedicated inference chips, or smaller models that run on modest hardware.
• Buying an “AI PC” or phone? The NPU matters. Vendors quote AI performance in TOPS (trillions of operations per second); higher TOPS means more on-device AI headroom, though real-world software support matters more than the number alone.
• Building edge or IoT hardware? Look at low-power edge AI chips where energy efficiency, not peak speed, is the priority.

Frequently asked questions