
Performance and Scalability
Training large transformer models and deploying them to production present various challenges.
During training, the model may require more GPU memory than available or exhibit slow training speed. In the deployment 
phase, the model can struggle to handle the required throughput in a production environment.
This documentation aims to assist you in overcoming these challenges and finding the optimal setting for your use-case. 
The guides are divided into training and inference sections, as each comes with different challenges and solutions. 
Within each section you'll find separate guides for different hardware configurations, such as single GPU vs. multi-GPU 
for training or CPU vs. GPU for inference.
Use this document as your starting point to navigate further to the methods that match your scenario.
Training
Training large transformer models efficiently requires an accelerator such as a GPU or TPU. The most common case is where 
you have a single GPU. The methods that you can apply to improve training efficiency on a single GPU extend to other setups 
such as multiple GPU. However, there are also techniques that are specific to multi-GPU or CPU training. We cover them in 
separate sections.