GPU passthrough is a virtualisation method used to directly assign a physical graphics processing unit to a single VM, ultimately bypassing the virtualisation software layers like the NVIDIA Virtual GPU (vGPU) manager. In this configuration, the physical hardware is accessed exclusively by the driver running inside that specific VM and is not shared with other environments. While a single VM can be assigned multiple physical GPUs depending on hypervisor limits, individual physical GPUs cannot simultaneously host virtual GPUs and perform passthrough.
This architectural limitation creates challenges for local development setups. For instance, I was trying to run a simple Kubernetes cluster through Docker Desktop on my Apple Silicon Macbook and I had encountered a now obvious hardware limitation because macOS container runtimes operates inside a Linux VM via the Hypervisor framework. This setup cannot natively pass through the Apple Silicon GPU using Metal to containers. As a result, running even a simple, small qwen2.5:7b model relied entirely on CPU software inference, which easily maxed-out the processor cores and caused noticeable performance bottlenecks. Like, I was unable to perform even the most basic tasks with this local model.
To address hardware isolation and GPU utilisation constraints in production environments, tools like Edera allow direct NVIDIA and AMD GPU passthrough into isolated Edera zones. By configuring hardware identifiers in the protect daemon, binding devices to the appropriate drivers, and launching zones with dedicated kernel variants, users can run GPU-accelerated workloads and verify hardware access directly through utilities like nvidia-smi.
Bryan Steiner has a really useful tutorial for GPU passthrough, if this is interesting to readers:
https://github.com/bryansteiner/gpu-passthrough-tutorial