AI accelerators are specialized expansion cards that look like GPUs and are explicitly designed to run AI inference. They don't have input or output ports; they sit in a PCIe slot or come as M.2 devices. These cards are ideal for handling tasks that don't require the full power of a GPU, such as object detection, transcription, and small language model processing. They are particularly useful for low-power vision tasks that don't need a full GPU. The processors inside these cards are optimized for parallel processing and matrix multiplication, operations used in AI workloads. An example of an AI accelerator is the Hailo-8 PCIe, which can handle up to 208 TOPS with a power draw of around 20W. While they offer significant power savings, their high cost often makes them less attractive compared to a GPU, especially when a GPU can already handle these tasks. The main selling point for these cards is their ability to lower the total power draw, but this doesn't always justify their high price. For instance, the Hailo-8 PCIe card, which costs around $400, can handle up to 206 TOPS, while the RTX 5060, which costs the same, can handle up to 614 TOPS. In this context, the Hailo-8 PCIe card is more cost-effective for tasks that don't require the full power of a GPU. However, for tasks that can be handled by a GPU, the Hailo-8 PCIe card may not be the best investment. In summary, AI accelerators are a cost-effective solution for lighter AI tasks, but they may not always be the best choice for tasks that can be handled by a GPU. It's important to evaluate the specific needs of your project and the available hardware options before making a decision.
