What each one is
A graphics processing unit, or GPU, began as a chip for drawing images. Today it is a general-purpose parallel processor that runs many kinds of code. For machine learning, its many cores handle the large batches of math that come up in training. Training is the step where a model learns from data, and inference is where a trained model answers requests.
A tensor processing unit, or TPU, is a chip built mainly for matrix math, the multiplication of grids of numbers that neural networks rely on. Tensors are blocks of numbers arranged in rows, columns and higher dimensions. The chip does that multiplication quickly and in bulk. It trades flexibility for that speed, so it handles fewer kinds of work well.
How well your work fits the chip
The deciding difference is how well your work fits the chip's design. A GPU is flexible, so it handles research code, custom layers and mixed jobs with little friction. The tensor processing unit is narrower, and it pays off when your workload is mostly the large matrix math that standard models rely on. Your answer comes from that fit, not from raw chip speed alone.
Cost follows the same logic. Total cost depends on how long a job runs and how busy the hardware stays, so compare cost per finished run instead of the hourly rate. Check your provider's own documentation for current rates, because they change.
When a GPU wins
Pick a GPU when your code changes often or uses operations that are not standard. Research teams usually benefit from the wide range of frameworks, libraries and debugging tools that run on it. The guide to choosing a GPU walks through the trade-offs.
- Your models use custom operations or unusual layers.
- You want the widest choice of providers, hardware sellers and tools.
- Your team runs a mix of training, inference and other GPU work on the same hardware.
When a tensor processing unit wins
A tensor processing unit makes sense when your models are large, stable and built on standard deep learning patterns. At that scale, the chip's focus on matrix math can make training and serving more efficient. Results depend on the model, so measure both options on your own workload before you commit. Ask providers for benchmark results on a model close to yours.
- Your training runs are long, steady and use standard layers.
- You serve a large model where total work finished over time matters more than flexibility.
- Your team already works in a toolchain that supports the chip well.
Software support and vendor dependence
Software support is where the chips differ most in daily work. GPU software is broad, and many machine learning libraries support it. Tensor processing units depend on a narrower set of compilers and frameworks. Some model types need extra work on them, and some may not run at all.
Availability and vendor dependence come next. GPUs are offered by many providers, and GPU providers differ in terms, so check each provider's own documentation before you plan. Tensor processing units are usually reached through a single provider or a small group of them. Code tuned for a specific chip's compiler, the translator that turns code into chip instructions, is harder to move later. Weigh that cost early, since switching later often means rework.
Which to pick
The right choice depends on who you are and what you are building. Each reader type below has a sensible starting point. Confirm current availability and terms with the provider before you commit.
- Research teams that change models often should start with a GPU.
- Developers learning machine learning should start with a GPU, since examples and tools are easiest to find for it.
- Small teams serving a stable, standard model should benchmark a tensor processing unit against a GPU.
- Teams with long training runs and engineers who know the compiler stack can evaluate both, using results from their own workload.
If you are unsure, begin with a GPU and keep the option open. For more background, read the GPU topic guide.