An inference chip is hardware specialised for the second half of an AI model's life, serving answers from a model that is already trained. Training teaches the model once, on big clusters; inference runs it millions of times after, and that repeated work is what an inference chip is shaped to do cheaply and fast.
In plain language
An AI model has two very different jobs in its life. First it is trained, fed enormous amounts of data until its internal numbers settle into something useful. That happens once, on large clusters, and it is expensive. Then it is used, over and over, every time someone sends a prompt and waits for an answer. That second job is called inference, and it is where an inference chip earns its name.
The reason inference deserves its own hardware is volume. A model like a large language model is trained one time but answered with billions of times. Each answer is mostly the same kind of maths, large multiplications of number grids, repeated for every word the model produces. A chip that does only that pattern, and skips the flexibility a general processor needs, can serve more answers per second and use less power doing it.
In practice the family is broad. The GPU most people associate with AI is a strong all-rounder for both training and inference. Beyond it sit chips aimed squarely at serving, such as the TPU line and a growing set of custom designs, including the LLM-optimised inference chip that OpenAI and Broadcom described in 2026. They differ in detail, but they share a goal, move a trained model's repeated arithmetic as cheaply as possible.
For a beginner the cleanest way to hold this is by contrast. Training is the classroom, slow, done once, very heavy. Inference is the working day, fast, done constantly, and far more sensitive to cost because it never stops. An inference chip is the tool built for the working day, and it sits inside the cloud computing systems and serverless backends that serve AI features to ordinary apps.
An everyday picture
Think of training as writing and printing a cookbook, slow, costly, done once. Inference is a line cook making the dishes from that finished book during a dinner rush. A general processor is a versatile cook who can also do the books and the ordering. An inference chip is the cook hired to do one thing, plate the same recipes as fast as the orders come in.
Where it shows up
Inference chips sit in the data centres behind AI assistants, search, recommendation feeds, voice transcription, image generation, and the AI features bolted onto everyday apps. Smaller cousins also run on phones and cameras, where the same idea, run a trained model efficiently, has to fit in a battery-powered device.
A small example
When you ask a chatbot a question and the reply streams back word by word, each of those words is produced by an inference chip in a data centre running the trained model. The model was trained months earlier and never changes during your chat, the chip is simply replaying it, fast, for you and for thousands of other people at the same time.
Common misunderstanding
One line to take with you
Training builds the model once, inference runs it forever, and an inference chip is hardware shaped for that forever. When AI moves from demo to product, the bill is mostly inference, which is why this kind of chip keeps getting purpose-built.
Frequently asked
They serve the two halves of a model's life. A training chip handles the one-time, very heavy job of teaching a model from data, which needs high precision and huge memory bandwidth. An inference chip handles the ongoing job of running the finished model to produce answers, which happens constantly and is judged mostly on cost, speed, and power per answer. A GPU can do both, but chips tuned only for inference trade away training flexibility to serve more answers cheaply. So the split is less about better or worse and more about which job the hardware is shaped for.
A GPU can run inference, and a lot of AI inference today happens on GPUs, so in that sense it acts as one. But GPU usually refers to a flexible processor that is also strong at training and at graphics, while inference chip describes hardware specialised mainly for serving trained models. Think of GPU as a capable generalist and a dedicated inference chip as a specialist, the categories overlap rather than exclude each other.
Because inference is the part of AI that never stops. A model is trained once but answered with for as long as the product lives, so over time the running cost is dominated by inference, not training. A chip designed around the exact maths a company's models repeat can aim to lower that running cost and power draw. That is the motivation behind custom efforts such as the LLM-optimised inference chip OpenAI and Broadcom unveiled in 2026. Whether a custom chip wins out depends on the model and software around it, so the decision is an engineering trade-off, not a guaranteed saving.