TRL has introduced co-located vLLM to improve the efficiency of training large language models by allowing both training and inference to run on the same GPUs, eliminating idle time and reducing hardware costs. This integration enhances throughput, simplifies deployment, and makes the system more robust for online learning setups like GRPO. The new approach is supported by a series of performance experiments demonstrating significant speedups compared to traditional server setups.

A comprehensive guide for deploying AI models using vLLM on Azure Kubernetes Service (AKS) with NVIDIA H100 GPUs and Multi-Instance GPU (MIG) technology is provided. It outlines the necessary prerequisites, steps for infrastructure creation, GPU component installation, and model deployment, enabling efficient utilization of resources and cost savings through hardware isolation.