Share one physical GPU across every virtual machine on your Proxmox cluster — on the consumer cards you already own, from any vendor, with no per-GPU licence. A Proxmox driver that makes your hypervisor AI-ready, plus a live web manager that shows real GPU & CPU telemetry — per model, per node, in real time.
This is the ComputeEngine manager driving a real Proxmox cluster. We task an AI node with a language model and watch one consumer iGPU light up — live GPU %, VRAM, and the exact model that's resident, per node.
Splitting one card across many virtual machines is a solved problem — if you can stomach the licence tax and the hardware it demands. ComputeEngine was built because we couldn't.
The incumbent charges $10–250 per user, per year, forever just to slice a card you already bought. Stop paying and the GPU stops working.
Their sharing refuses to run on the consumer GPUs in your rack. You're pushed into one vendor and expensive datacenter-only cards to do something your hardware can already do.
The free alternative bolts one whole GPU to one VM. Every other machine on the host gets nothing — an expensive card sitting 90% idle.
The web manager isn't a status page — it's a live map of your GPU fabric. Physical GPUs flow into compute units flow into model endpoints, and the whole graph lights up in real time as work moves through it.
Real GPU engine %, VRAM used, and host-core load streamed over WebSocket — sub-second, straight from the nodes. No agents to babysit, no polling lag.
GPU and CPU sit side by side on every node. The connector to the busy tile lights, so you see at a glance whether a job landed on the accelerator or fell back to cores.
Every VM's model endpoints are listed in-place — and the one that's actually resident and serving glows, while the rest stay dark. The fleet's real state, at a glance.
Watch a single physical card feed multiple compute units. Dedicated or pooled, the topology is drawn from live data — not a diagram you have to keep up to date.
Point the manager at any node and pull an Ollama model straight from the dashboard. It lands on the node, loads into the vGPU, and appears in the graph.
The manager carries its own payload and can push-install ComputeEngine to a peer node over SSH — binaries, scripts, GPU preflight and all — from one place.
ComputeEngine turns a single physical GPU into a shared resource for the virtual machines on your Proxmox cluster — so your homelab or small fleet can run local LLMs, AI inference, and GPU-accelerated desktops without buying a card per VM or renting one in the cloud.
A lightweight piece on every host, and one web app to rule them all.
A Proxmox driver that makes your hypervisor ready for the AI world. Install it on each host and that machine's GPU becomes a shared resource for its VMs — a lightweight vGPU per guest, no datacenter card, no licence, no cloud. A self-healing preflight even reclaims a card that was locked to a single VM.
Run the entire fleet from a web app. See live GPU/CPU telemetry, watch which model is resident on which node, pull new models, and provision fresh nodes over SSH — all from one dashboard, no command-line archaeology, no per-host fiddling.
Build once, push to your nodes over SSH, and bring up the manager. The install lays everything out under /opt/ce and symlinks the CLIs — three commands and you're sharing silicon.
# 1 · push the driver to a node (free the GPU, enable sharing, run the manager) $ SSHPASS=… PREFLIGHT=1 WITH_MANAGER=1 \ scripts/install/ce-node-deploy.sh 10.0.110.246 10.0.110.247 10.0.110.248 ✓ /opt/ce laid out · CLIs symlinked · GPU freed · manager live on :8443 # 2 · (or) let the manager provision a peer node for you $ ce-orchestrator install-node --host 10.0.110.248 --user root --preflight # 3 · stamp a GPU-accelerated AI node (Ubuntu + Ollama on the shared GPU) $ VRAM=4G ./ce-ai-node.sh 9101 ✓ ai-node-9101 up · vGPU attached · Ollama serving
One push over SSH lands the binaries, self-heals the GPU (reclaims a card locked to one VM), and brings up the GPU-sharing stack.
WITH_MANAGER=1 runs the orchestrator as a service. Open :8443 and your live fabric is there.
Stamp reproducible GPU VMs that run local LLMs on the shared card — one command per node, cluster-wide.
Sharing a GPU across virtual machines isn't new — paying a per-user licence and buying datacenter-only cards to do it is the catch. No one else gives you free + consumer cards + any vendor + native VM sharing at once.
| Solution | Licence cost | Shares 1 GPU across VMs |
Consumer cards |
Any vendor |
VM / Proxmox-native |
Live migration |
Audience |
|---|---|---|---|---|---|---|---|
| NVIDIA vGPU / GRID | $10–250 / user / yr | ✓ | ✗ | ✗ | ~ | ✓ | Enterprise |
| VMware vSphere vGPU | VMware + NVIDIA fees | ✓ | ✗ | ✗ | ✗ | ✓ | Enterprise |
| VMware Bitfusion | Discontinued (EoL 2025) | — | — | ✗ | ✗ | — | — (dead) |
| HAMi | Free (open source) | ✓ (containers) | ✓ | ✓ | ✗ | ✗ | Kubernetes / AI |
| KAI Scheduler (run:ai) | Free (open source) | ✓ (containers) | ✓ | ~ | ✗ | ✗ | Kubernetes / AI |
| GPU passthrough (VFIO) | Free | ✗ 1 GPU = 1 VM | ✓ | ✓ | ✓ | ✗ | Homelab |
| ComputeEngine | No per-GPU licence | ✓ | ✓ | ✓ | ✓ | v2 ☉ | Homelab / SMB |
✓ full · ~ partial / conditional · ✗ not supported · — n/a · v2 ☉ on the roadmap. Live migration (moving a running, GPU-backed VM between nodes with no downtime) is planned for ComputeEngine v2. Competitor data from vendor primary sources, 2026 (NVIDIA vGPU licensing & pricing guide, pve.proxmox.com vGPU wiki, project-hami.io, developer.nvidia.com KAI, Broadcom/VMware Bitfusion end-of-life notices). NVIDIA vGPU is a per-concurrent-user subscription ($10 vApps / $50 vPC / $250 vWS per user/yr) on datacenter-only cards; Bitfusion reached end of support May 2025.
No per-GPU licence, no datacenter-card tax, built for the hardware you already run. Grab it from your portal, point the manager at your nodes, and start sharing.