Digital Event Horizon
Revolutionize your AI research with the latest innovation from Hugging Face: co-located vLLM training. Discover how this game-changing technology can boost efficiency, accuracy, and scalability in large language model training.
Hugging Face introduces co-located vLLM training, a game-changing technology that enables efficient use of GPUs in deep learning models. The concept of co-location embeds vLLM within the same process group as the training code, sharing GPUs and minimizing inter-process communication. Benefits include reduced idle time, minimized inter-process communication, and seamless compatibility with torchrun. Co-location provides robust inter-process communication, enabling efficient and scalable systems for large-scale training runs. The approach preserves accuracy by maintaining model quality across multiple benchmarks. Challenges include tensor parallelism bugs and level 2 sleep buffer bugs, but the technology still offers significant efficiency gains.
Hugging Face, a leading provider of artificial intelligence and machine learning tools, has revolutionized the field of large language model training with its latest innovation: co-located vLLM training. This game-changing technology allows for efficient use of GPUs in deep learning models, paving the way for faster and more accurate training of complex AI systems.
The concept of co-location is simple yet profound. Instead of using separate GPUs for training and inference, as was previously done, co-located vLLM training embeds vLLM within the same process group as the training code. This enables both tasks to share the same GPUs, taking turns smoothly without wasting resources or requiring extra hardware.
The benefits of co-location are multifaceted and far-reaching. Firstly, it reduces idle time by allowing training and inference to share the same GPUs. Secondly, it minimizes inter-process communication by eliminating the need for REST API calls or networking. Thirdly, it ensures seamless compatibility with torchrun, making it easy to scale across nodes with minimal configuration changes.
Furthermore, co-location provides robust inter-process communication, which avoids the complexity of setting up distributed process groups between independent processes, as required in server mode. This results in a more efficient and scalable system that can handle large-scale training runs without sacrificing performance.
In addition, co-located vLLM training preserves accuracy by maintaining model quality across multiple benchmarks. The Math500 benchmark, which compares the performance of base models, co-locate-trained models, and plain-trained models, demonstrates the efficacy of this approach. Both co-locate-trained models outperform the base model, and the co-locate model performs on par with the plain-trained model.
However, this breakthrough technology also presents several challenges and lessons learned. Tensor Parallelism bug in vLLM version 0.8.0 and above caused issues, requiring careful debugging and version control to resolve. Level 2 sleep buffer bugs were also encountered, which required explicit logic to restore buffers when waking up from level 2 sleep.
Moreover, segmentation faults on exit during shutdown remain an open issue with vLLM sleep, but this did not hinder the completion of demos and experiments shared in the blog. Nevertheless, a fix is being awaited before integrating sleep() fully into TRL upstream.
Despite these challenges, co-located vLLM training offers significant efficiency gains by improving GPU utilization dramatically. It also enables the use of DeepSpeed ZeRO Stage 3, which allows extremely large networks to fit into memory by distributing model weights, gradients, and optimizer states across multiple GPUs. This results in reduced memory fragmentation and freed-up critical GPU memory.
In conclusion, co-located vLLM training represents a significant leap forward in AI research, enabling faster and more accurate training of complex language models. As this technology continues to evolve, it will unlock new possibilities for researchers and developers working with deep learning models.
Related Information:
https://www.digitaleventhorizon.com/articles/No-GPU-Left-Behind-The-Revolution-of-Co-located-vLLM-Training-deh.shtml
https://huggingface.co/blog/vllm-colocate
https://github.com/huggingface/trl/issues/3064
https://huggingface.co/docs/trl/main/en/vllm_integration
Published: Tue Jun 3 13:00:00 2025 by llama3.2 3B Q4_K_M
