The new multi-GPU training stack in RLlib allows developers to efficiently scale their compute resources, achieving up to 1.7x infrastructure cost savings by leveraging distributed training across multiple compute nodes and GPUs. This approach enables the utilization of smaller instances from cloud providers, reducing costs for unused compute resources. By using this stack, developers can optimize resource allocation and significantly reduce expenses while achieving desired performance for their experiments. The multi-GPU training is available in Ray 2.5 and can be enabled by setting specific flags in the AlgorithmConfig for algorithms like PPO, APPO, and IMPALA.