Yes, though how you do it depends on what you're doing.
I do a lot of training of encoders, multimodal, and vision models, which are typically small enough to fit on a single GPU; multiple GPUs enables data parallelism, where the data is spread to an independent copy of each model.
Occasionally fine-tuning large models and need to use model-parallelism, where the model is split across GPUs. This is also necessary for inference of the really big models, as well.
But most tooling for training/inference of all kinds of models supports using multiple cards pretty easily.
I do a lot of training of encoders, multimodal, and vision models, which are typically small enough to fit on a single GPU; multiple GPUs enables data parallelism, where the data is spread to an independent copy of each model.
Occasionally fine-tuning large models and need to use model-parallelism, where the model is split across GPUs. This is also necessary for inference of the really big models, as well.
But most tooling for training/inference of all kinds of models supports using multiple cards pretty easily.