Researchers have proposed a method to improve the efficiency of large language models by co-locating them on multiple GPUs within a single node. This approach, called vLLM in TRL, allows for more efficient use of resources and can potentially unlock new levels of performance. The idea is based on the observation that current GPU architectures are not optimized for the unique memory access patterns of large language models. By co-locating these models, researchers aim to reduce memory latency and improve overall system efficiency. --- Why it matters: This matters because it could lead to significant improvements in the performance and efficiency of large language models, which are critical components of many AI applications. Source: https://huggingface.co/blog/vllm-colocate