What is AI Accelerators
Specialized computing devices that speed up the training and execution of artificial intelligence models.
Definition
AI accelerators are needed because modern models perform a huge number of matrix operations. A conventional processor does not always cope efficiently, so graphics processors, tensor processors, neural accelerators and other specialized chips are used. They are especially important for large language models, computer vision, and generative services.
Example
The image generation service uses accelerators to process user requests faster and avoid making them wait too long for each image.
Why it matters
Understanding the term helps to evaluate the cost, speed and scalability of AI services: the quality of the model is not only the algorithm, but also the hardware.
How it works
The accelerator performs parallel numerical operations faster and more energy-efficiently than a general-purpose processor. In infrastructure, it is used in conjunction with memory, storage, networking, and software libraries.
Where it is used
- training large models
- launch of generative services
- image and video processing
Limitations
Accelerators are expensive, require a suitable software stack, and can be in short supply. Not every task benefits from specialized equipment.