Concept dictionary
GPU / TPU
GPUs and TPUs are types of processors designed to perform many mathematical operations in parallel, making them useful for training and running many AI models.
- Review state
- Needs review
- Freshness
- Current
- Version
- 2
Also called
- GPU / TPU
- GPU and TPU
- Graphics Processing Unit
- Tensor Processing Unit
Remember it this wayAccelerators perform the parallel math; models and systems decide how it is used.
In plain language
A graphics processing unit, or GPU, originated in graphics workloads and became widely used for machine learning. A tensor processing unit, or TPU, is a specialized accelerator designed for tensor-heavy machine-learning computation.
Why it matters
Accelerator availability, memory, software support, energy use, cost, and supply chains influence which models can be built and where they can run.
Example
A team trains a neural network across several GPUs, then chooses a smaller accelerator configuration for production inference.
Common misconception
A GPU or TPU is not an AI model and chip labels alone do not establish complete system performance.
Related terms
Sources
- NVIDIA, CUDA C++ Programming Guidevendor-primary documentation for GPU parallel-computing architecture and programming.
- Google Cloud, Introduction to Cloud TPUvendor-primary documentation for tensor processing units and machine-learning workloads.
Learn more
- Place accelerators within the broader control analysis in [[layered-sovereignty|Layered Sovereignty]].