# No GPU left behind: Unlocking Efficiency with Co-located vLLM in TRL

DevFeed: [No GPU left behind: Unlocking Efficiency with Co-located vLLM in TRL](<https://devfeed.tech/articles/no-gpu-left-behind-unlocking-efficiency-with-co-located-vllm-in-trl-7558.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/vllm-colocate>)

Author: Mert Toslali; Yu Chin Fabian Lim; Quentin Gallouédec; Ed Snible; Raghu Ganti; Mudhakar Srivatsa

Published: 2025-06-03T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [vllm](<https://devfeed.tech/topics/vllm.md>), [trl](<https://devfeed.tech/topics/trl.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>)

Tags: [efficiency](<https://devfeed.tech/tags/efficiency.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [grpo](<https://devfeed.tech/tags/grpo.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm](<https://devfeed.tech/tags/llm.md>), [performance](<https://devfeed.tech/tags/performance.md>), [rlhf](<https://devfeed.tech/tags/rlhf.md>), [train](<https://devfeed.tech/tags/train.md>), [training](<https://devfeed.tech/tags/training.md>), [trl](<https://devfeed.tech/tags/trl.md>), [vllm](<https://devfeed.tech/tags/vllm.md>)

## AI overview

This article explains how TRL integrates vLLM to accelerate GRPO training of LLMs. It describes the GPU inefficiencies caused by running training and generation on separate devices and introduces colocated vLLM, which allows both tasks to share GPUs within the same distributed process group.

## Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.