Provider nodes
Hardware requirements for VM and pod nodes, GPU setup, health checks, and the full lifecycle of your provider hardware.
This page covers what a provider box needs, how each runtime sets up GPUs, how to confirm a node is healthy, and how nodes behave over their lifetime. To bring your first node online, start with Providers — onboarding is one command. Everything here is handled for you by that command; it's documented so you know what's happening and how to operate the box afterwards.
Two node types, two sets of requirements
Read Two node kinds, detected automatically
before buying or preparing hardware. A VM node (qemu) needs bare metal, KVM
and IOMMU; a pod node (runc) needs none of those and runs happily inside a
virtualized guest. The installer detects which one the box is; the requirements
below are split accordingly.
Hardware requirements
Both node types
| Requirement | Detail |
|---|---|
| OS | Ubuntu 22.04 or 24.04 LTS, with root / sudo. This is what we test and support; the installer does not enforce a version, and it has dnf fallbacks, but anything else is unsupported. |
| Network | Outbound HTTPS (443) — the node dials out, so no inbound ports are required. Outbound UDP must also work: the data-plane tunnel is QUIC and the overlay uses UDP/4242. A security group that blocks outbound UDP produces a node that enrolls fine and then never carries traffic. |
| Tools | curl and sha256sum must already exist; the installer pulls jq, tar, unzip, pciutils and installs Docker itself. |
| Subnet | 192.168.249.0/24 must be free. The control plane allocates guest IPs from it (hardcoded), so a host already using that range breaks workloads in ways that look random. |
VM nodes (qemu)
| Requirement | Detail |
|---|---|
| Bare metal | Required — passthrough cannot work in a normal cloud guest. |
| CPU virtualization | Intel VT-x / AMD SVM — /dev/kvm must exist. |
| IOMMU | Intel VT-d / AMD AMD-Vi, enabled in BIOS (set Enabled, not Auto). |
| GPU | NVIDIA or AMD. CPU-only VM nodes are also welcome. |
| RAM | No minimum is enforced, but RAM is what caps how many VMs a node can host: the scheduler fit-checks each VM's memory against the node's, and a box that runs out simply stops being placed on. 32 GB is a realistic floor, 64 GB+ recommended. |
| Disk | The health check fails below 20 GB free on / and warns below 50 GB; for VM image storage it fails below 50 GB and warns below 200 GB. |
Pod nodes (runc)
| Requirement | Detail |
|---|---|
| Virtualization | Bare metal or a VM guest. No KVM, no IOMMU, no VT-d needed. |
| NVIDIA driver | You install it. The bootstrap does not install a GPU driver, and a pod node is unusable without one: nvidia-smi must work before you enroll. The NVIDIA container toolkit is installed for you. |
| GPU visibility | Every GPU you intend to sell must appear in lspci and nvidia-smi. On a guest, that means passthrough is already configured by your hypervisor. |
| GPU model | The GPU's PCI id must be in the platform catalog, otherwise the node enrolls but sells nothing. The preflight tells you before you enroll. |
| Disk | The preflight fails below 200 GB free for the weights cache (/var/lib/openrelay) and 100 GB for the Docker image store (/var/lib/docker). |
| RAM | No minimum is enforced. Size it for the models you intend to serve: weights are staged through host memory and each concurrent pod holds its own working set. |
Remote or separate storage must be mounted before you enroll
If the weights cache or Docker store is meant to live on a separate NVMe or a remote/network mount, mount it first and make it boot-ordered. The platform creates the directory if it is missing, so an unmounted volume silently lands on your boot disk — the node keeps working, slowly, and the only symptom is a performance cliff.
Enroll such a node with WEIGHTS_CACHE_REQUIRE_MOUNT=1 so the node refuses to
stage weights onto the boot disk instead of quietly demoting itself. In /etc/fstab
use _netdev, nofail, and x-systemd.requires-mounts-for so a late mount cannot
lose the race against Docker and Nomad.
GPU setup
VM nodes: passthrough (QEMU + VFIO)
The one-command install configures passthrough for you — you don't hand-edit GRUB or modprobe files. On a GPU box it:
- Detects your NVIDIA/AMD GPUs with
lspci(works even with no driver installed). - If they aren't passthrough-ready, enables IOMMU on the kernel command line,
binds each GPU (and its IOMMU-group siblings) to
vfio-pci, and installs a boot-time service (vectorlay-vfio-bind.service) so the binding survives reboots. - Asks you to reboot and re-run the exact same command (the installer exits
75). The node is intentionally not enrolled until passthrough actually works — we never advertise a GPU that can't run a VM.
If, after a reboot, the GPUs still aren't bound, enable virtualization + IOMMU in your BIOS/UEFI, then reboot and re-run:
- Intel — enable VT-x and VT-d.
- AMD — enable SVM and IOMMU (set to Enabled, not Auto).
A passthrough GPU leaves the local console
Once a GPU is bound for passthrough it no longer drives a local display. Manage the box over SSH, serial, or IPMI — not a monitor.
The unit of allocation is the IOMMU group: GPUs that share a group are always passed to a VM together. For full-node (all-GPU) allocations, NVSwitches are passed through too so NVLink works — this is automatic.
Pod nodes: native driver, no VFIO
A pod node does the opposite of the above, and does it automatically:
- The host NVIDIA driver is kept. The VFIO conversion self-skips when
nvidia-smiis present, so your driver is never torn down. - The NVIDIA container toolkit is installed and wired into Docker so
--gpusworks, along with a userns-remapped Docker daemon and the platform bridge. - No IOMMU groups are required. Cloud and virtualized guests usually expose none; the node then advertises one allocatable unit per GPU, which is exactly how pods consume them.
- The
vfio-gpuNomad plugin is not installed, because it would compete with the container runtime for the same GPUs.
Each pod gets one GPU
A pod is scheduled onto a single GPU unit. An 8-GPU box therefore sells 8 independent single-GPU pods, not one 8-GPU pod. That is a throughput consideration, not a limitation on how many GPUs you can bring.
Check node health
Run the check that matches your node type. Running the wrong one is the most common source of confusing output.
sudo vectorlay-node-doctorIt prints PASS / FAIL per check and a final verdict:
- Agents & tunnel —
nebula,nomad, andnode-agentrunning; auto-update timer enabled. - Virtualization —
/dev/kvmpresent. - GPU passthrough — IOMMU active and per-IOMMU-group VFIO readiness, ending with
N/M GPU unit(s) ready to rent. - Disk — enough free space for VM base images.
A green doctor means the control plane will advertise the node's GPU units.
- If a unit shows WEDGED, a GPU fell off the bus (the D3cold reset bug) — power-cycle the host (a soft reboot may not clear it).
- If a unit isn't bound to
vfio-pci, runsudo systemctl restart vectorlay-vfio-bindor re-run the bootstrap.
Node lifecycle
Two credentials (don't conflate them)
Provisioning token (vtk_…) | Durable identity | |
|---|---|---|
| What | Short-lived bootstrap key | Data-plane mTLS cert + durable node token |
| Lifetime | 24 hours; enrolls any number of nodes until then | Lives as long as the node |
| On disk? | No — passed to the installer, used, discarded | Yes — under /etc/vectorlay/ |
| Used for | Enrollment only | Every gateway tunnel + node-agent self-update |
Everything after enrollment runs on the durable identity — the provisioning token expiring later is by design and harmless.
Reboots & power loss
systemd brings nebula, nomad, and node-agent back at boot; they read their
credentials from disk and rejoin automatically. On VM nodes the VFIO bind service
re-binds the GPUs before Nomad starts. No action needed.
Automatic updates
The node-agent keeps itself current via a systemd timer (hourly, with up to
20 minutes of jitter, first run 10 minutes after boot), authenticated with the
durable node token. Updates drain in-flight HTTP/SSH first, install
atomically, and roll back on failure — live sessions survive.
- Opt out per node: enroll with
NODE_AGENT_AUTOUPDATE=0, orsudo systemctl disable --now node-agent-update.timer. - Force a check now:
sudo systemctl start node-agent-update.service(logs:journalctl -u node-agent-update).
Recycle / re-enroll
Re-running the onboarding one-liner on an existing box is safe and idempotent: the node keeps its identity (same node ID and overlay IP), its token is rotated, no quota is consumed, and nothing referencing the node (clusters / VMs) breaks. Use it to repair a node or roll it onto new config.
The node kind follows the hardware
The installer detects the node kind from the box's shape, so re-enrolling the same
hardware keeps the same runtime. Converting a box between VM and pod means changing
the hardware's story: undoing the vfio-pci binding and installing an NVIDIA
driver to turn a VM host into a pod node, so a reimage is usually the cleaner
path.
Take a node offline
To temporarily remove a node from the network, stop its agents:
sudo systemctl stop nomad nebula node-agentNomad stops scheduling to it and the data-plane tunnel closes (in-flight work drains first). Start the services again — or reboot — to bring it back.
Decommission a node
To retire a node permanently, set its status to removed from the Nodes view
in the dashboard, or via the API:
curl -fsSL -X PATCH -H "Authorization: Bearer $VL_KEY" \
-H "Content-Type: application/json" -d '{"status":"removed"}' \
https://api.openrelay.inc/v1/orgs/$VL_ORG/provider/nodes/$NODE_IDThis frees the node's workloads and GPU units. A decommission isn't a one-way door: re-running the onboarding one-liner on a removed node auto-recovers it.
Clean offboard (decommission the hardware)
The steps above retire the node in the control plane. Clearing the node-local state is a separate, deeper step you only need when you're decommissioning the hardware for good or moving the box to a different environment and re-onboarding it.
A node's durable identity, Nomad client state, overlay certificates, and config are
written to local disk at enrollment. If a box is re-onboarded — or pointed at a
different environment — without clearing this, the fresh enrollment collides
with the stale identity: the node's Nomad client is rejected and it gets stuck in
pending, never coming online. A clean offboard wipes that node-local state so a
new enrollment provisions cleanly.
This removes the node from the fleet
Stopping the agents and clearing state terminates any VMs and workloads currently running on the node. If those must be preserved, drain or migrate them first. Run every command below as root.
Stop the OpenRelay agents
sudo systemctl stop nomad nebula node-agent node-agent-update.timerClear Nomad client state, config, and TLS material
The stale Nomad client identity is the main thing that blocks re-onboarding.
sudo rm -rf /opt/nomad/data
sudo rm -rf /etc/nomad /etc/nomad.d
sudo rm -f /etc/systemd/system/nomad.service.d/intro.confClear the overlay (Nebula) identity
sudo rm -rf /etc/nebulaClear the OpenRelay node identity
The durable node token, data-plane certificates, signing key, and config.
sudo rm -rf /etc/vectorlay/dataplane
sudo rm -f /etc/vectorlay/node.token /etc/vectorlay/node-agent-update.env \
/etc/vectorlay/identity.key /etc/vectorlay/vfio-attempts(Optional) Remove per-VM overlays and seed data
Hygiene only. Base images can be left in place — they're checksum-verified and
reused. On a pod node these paths are typically empty; the equivalent hygiene step
is clearing the weights cache under /var/lib/openrelay/weights.
sudo rm -rf /var/lib/vectorlay/vms/* /var/lib/vectorlay/seed/*Reload systemd
sudo systemctl daemon-reloadWhich steps are essential
Steps 1–4 are required — leaving any of that stale identity or client state on
disk is what causes the stuck-pending problem on re-onboarding. Step 5 is
optional hygiene. If you're doing a full OS reimage instead, the fresh image
is already a clean slate, so steps 1–5 are unnecessary.
Re-onboarding after a clean offboard
Once the node-local state is cleared, re-onboard the box with the standard one-command installer. Any provisioning token that is still within its 24-hour lifetime works; mint a fresh one if the old token has expired or you revoked it:
curl -fsSL "https://api.openrelay.inc/v1/provider/bootstrap?token=<provisioning-token>" | sudo bashThe installer detects the node kind and GPUs on the box, so no extra params are needed. See Providers for how to mint a token and the full set of bootstrap options.
Troubleshooting
| Symptom | Cause / fix |
|---|---|
403 NOT_A_PROVIDER | The org isn't an approved provider yet. |
401 | Bad, missing, or expired API key / provisioning token. |
| Install prints nothing ("0 logs") | curl -fsSL … | sudo bash swallows HTTP errors, so an empty script runs. Re-run with curl -sS -w 'HTTP %{http_code}\n' … to see the real status. |
Installer exits 75 | Not a failure: the box was staged but needs a reboot before passthrough works. Reboot and re-run the same command. |
checksum mismatch / 502 ARTIFACT | A stale artifact — re-run; artifacts are re-fetched and verified each run. |
409 HOSTNAME_CONFLICT | Another node in the org already uses this hostname. Node identity is per box (not per hostname), so this means two different machines share a name — rename one and re-run. |
GPUs not bound to vfio-pci after reboot (VM node) | Enable VT-d / IOMMU in BIOS; sudo systemctl restart vectorlay-vfio-bind; reboot and re-run. |
| A GPU unit shows WEDGED | The GPU fell off the bus (D3cold reset bug) — power-cycle the host. |
| Pod node enrolled but sells nothing | Usually an unrecognized GPU model, GPUs sharing an IOMMU group, or GPUs bound to vfio-pci. Run sudo vectorlay-pod-node-preflight. |
| Pod node is slow / weights re-download | The cache volume probably isn't mounted, so the boot disk is being used. See the storage callout above. |
no /dev/kvm on a pod node | Expected — pod nodes aren't VM hosts. Use vectorlay-pod-node-preflight, not vectorlay-node-doctor. |
See Providers for onboarding and Authentication for API keys.