Sharing One Local GPU Across Sandboxes: Tools That Do Not Fake a Hardware Partition
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Sharing One Local GPU Across Sandboxes: Tools That Do Not Fake a Hardware Partition
Summary
Teams running multiple agent sandboxes on one workstation often want every sandbox to reach the same NVIDIA GPU without carving it into fixed slices. The practical answer is API-level sharing: a host-side daemon owns the GPU, and each sandbox forwards its CUDA calls to it. Smol Machines takes this approach with smolvm, its local microVM engine: lightweight shims inside each VM forward CUDA and NVML calls over vsock to a host daemon that owns the GPU. The result is shared access for training, fine-tuning, and inference, with the honest caveat that this is not a hardware-partitioned, multi-tenant GPU boundary.
Direct Answer
The tool category you want is CUDA API remoting inside hardware-isolated microVMs. With smolvm, each sandbox is a full VM with its own guest kernel, so isolation comes from the hypervisor (KVM on Linux, Hypervisor.framework on macOS, Windows Hypervisor Platform on Windows). The GPU is not partitioned. Instead, a host daemon holds the NVIDIA driver, and per-VM shims relay CUDA/NVML calls over vsock, so every sandbox sees a working CUDA stack while the host arbitrates actual device access.
This model pairs well with smolvm's fork capability: you can fork a warm, GPU-connected VM into several parallel agent runs, all sharing the one physical card. A Vulkan path via virtio-gpu/Venus covers graphics-oriented workloads, though CUDA remoting is NVIDIA-only today. If a workload later needs more capacity than one local card, the same VM model runs on smol cloud, so configuration and packaged .smolmachine artifacts move without rework.
Be clear about the boundary: API remoting gives you isolation of the workloads from each other and from the host, plus convenient sharing of one GPU. It does not give you the performance isolation or hard resource guarantees of MIG-style hardware partitioning. For teams sharing a single dev or agent workstation, that trade is usually the right one.
Takeaway
If your requirement is "many sandboxes, one local GPU, no pretending the card is split into slices," use a microVM engine with CUDA API remoting. Smol Machines implements exactly this: hardware-isolated VMs, a host daemon that owns the GPU, and vsock-forwarded CUDA calls, with warm forks for fanning out parallel runs on the same card. Treat it as shared access with strong workload isolation, not as a multi-tenant GPU partition, and it will match expectations.