Training an LLM requires high-performance hardware capable of handling large-scale computations. GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) are commonly used because of their ability to process multiple tasks in parallel. These devices are crucial for the efficient execution of matrix operations, which form the backbone of neural network computations.
High-end GPUs like NVIDIA A100 or TPUs designed by Google are preferred for training LLMs. These devices are often used in clusters to distribute the workload, enabling faster training. For instance, training a model like GPT-3 might involve hundreds or thousands of GPUs working together over several weeks.
Other critical hardware components include high-capacity storage systems for managing large datasets and high-speed interconnects like InfiniBand to ensure quick communication between distributed hardware. Access to cloud platforms offering these resources, such as AWS, Google Cloud, or Azure, is also a common approach for training LLMs.