The number of GPUs that one needs for AI depends on the specific application and the size of the data being worked with. Generally, the more GPUs that are available, the faster the computation will be.
For small-scale projects, a single GPU may be sufficient. However, for larger projects that involve processing large datasets or complex neural networks, multiple GPUs may be needed to achieve optimal performance.
It’s also important to consider the type of GPU being used. Not all GPUs are created equal, and some may be better suited for AI workloads than others. Nvidia’s Tensor Cores, for example, are designed specifically for deep learning and can provide significant performance improvements compared to traditional GPUs.
Another factor to consider is whether to use GPUs in a distributed system. By distributing the workload across multiple machines that each have multiple GPUs, it’s possible to achieve even faster computation times. However, setting up a distributed system can be complex and requires an in-depth understanding of the underlying hardware and software components.
In summary, the number of GPUs that one needs for AI depends on the specific requirements of the project. For small-scale projects, a single GPU may be sufficient, while larger projects may require multiple GPUs or a distributed system. It’s important to also consider the type of GPU being used and whether it’s optimized for AI workloads.