Training GPT models involves massive amounts of data and complex computations, requiring high-performance GPUs with large VRAM capacity to handle the load efficiently. Ample memory is also crucial for storing model parameters and intermediate computations during training.
When deploying GPT models for inference tasks, specialized hardware accelerators like TPUs (Tensor Processing Units) or optimized inference frameworks may be necessary to ensure low latency and high throughput.
It is important to optimize the hardware configuration, parallelize computations, and utilize distributed training techniques to speed up the training process and reduce costs.