
Efficient Training on Multiple GPUs
If training a model on a single GPU is too slow or if the model's weights do not fit in a single GPU's memory, transitioning 
to a multi-GPU setup may be a viable option. Prior to making this transition, thoroughly explore all the strategies covered 
in the Methods and tools for efficient training on a single GPU as they are universally applicable 
to model training on any number of GPUs. Once you have employed those strategies and found them insufficient for your 
case on a single GPU, consider moving to multiple GPUs.
Transitioning from a single GPU to multiple GPUs requires the introduction of some form of parallelism, as the workload 
must be distributed across the resources. Multiple techniques can be employed to achieve parallelism, such as data 
parallelism, tensor parallelism, and pipeline parallelism. It's important to note that there isn't a one-size-fits-all 
solution, and the optimal settings depend on the specific hardware configuration you are using. 
This guide offers an in-depth overview of individual types of parallelism, as well as guidance on ways to combine 
techniques and choosing an appropriate approach. For step-by-step tutorials on distributed training, please refer to
the 🤗 Accelerate documentation. 