Running a vLLM LXC on Proxmox 9 with Nvidia GPU Passthrough (medium.com)

A recent guide detailed the process of running a vLLM (Variable Language Model) within a Linux container (LXC) on Proxmox 9, utilizing NVIDIA GPU passthrough to access GPU resources for AI applications. The author set this up on a modest Intel i5-12400 machine paired with an RTX 3050, demonstrating that even mid-range hardware can effectively run smaller models for tasks like text generation and coding assistance without relying on third-party APIs. This setup allows for resource-sharing among multiple services, enhancing efficiency for developers working in AI/ML environments. This development is significant as it highlights an accessible configuration for leveraging powerful GPU capabilities within lightweight containers like LXC, which can streamline workflows for AI practitioners. The guide details critical steps, such as installing necessary NVIDIA drivers, configuring the LXC for GPU access, and deploying vLLM for model interactions. By making these advanced configurations approachable, it encourages wider experimentation and innovation in AI and machine learning, especially for users looking to optimize their hardware setups for various AI tasks. The inclusion of tools like `nvidia-smi` and `nvtop` for monitoring GPU performance further empowers users to manage and fine-tune their AI workloads effectively.