Can this capacity enter service?
Qualify each node with the context of the fleet.
Use current evidence, prior incidents, and the operating standard your team already trusts.
Turn fleet history into better GPU decisions.
Harbor gives your cloud a reliability model that learns from fleet history, operator decisions, and verified outcomes. Qualify nodes, diagnose issues, and choose the next action with more context over time.
Fleet decision / node-14Representative
Current state8× H200
Degraded00
01
02
03
04
05
06
07
- Signal
- PCIe link below rated state
- History
- Prior physical-link incident
- Owner
- Fleet Ops / physical
Fleet-specific recommendationReview required
Run a targeted physical inspection and verification path.- 01 Drain node from usable capacity
- 02 Inspect and reseat the affected GPU
- 03 Verify the link and GPU under load
Why this action
Evidence, node history, and the approved procedure point to the same next step.
Your fleet already produces the signals. Harbor turns them into the next decision.
Existing tools tell you what changed. Harbor connects those signals to operating history and expert judgment, then gives the responsible team a specific recommendation it can inspect.
What your fleet already knows
- Telemetry
- Node history
- Runbooks
- Operator judgment
What Harbor returnsThe next action, grounded in the context of your fleet.
- Decision
- Targeted qualification path
- Reason
- Visible and inspectable
- Control
- Operator approved
Every verified outcome adds context to the next.
Harbor connects what the fleet showed, what the operator chose, and what happened after the work was done. The result is reliability intelligence shaped by the way your cloud actually operates.
Fleet-specific reliability modelOne continuous learning record
Evidence in
- Fleet signals
- Node history
- Operator decisions
HarborYour fleet's reliability model
Connects current evidence to the history of how your operation makes decisions.
Decisions out
- Qualify
- Diagnose
- Recommend
- Verify
Verified outcomeFleet memory
The result becomes context for what happens next.
Existing telemetry and workflows stay in place. Harbor adds the context that helps teams choose and verify the next action.
Better decisions across the life of every node.
One fleet-specific model supports the decisions that determine whether capacity is useful, risky, or ready to serve customers again.
What is actually wrong?
Diagnose the incident, not the alert.
Connect workload symptoms to infrastructure evidence and the history of this node and others like it.
What should happen next?
Recommend the smallest safe response.
Give the responsible operator a specific next action, the reason behind it, and the evidence to inspect.
Is it ready to return?
Verify the outcome before capacity goes back into service.
Keep the response and the result attached to the incident so the next decision starts with more context.
See the loop in one GPU incident.
The evidence, recommendation, operator decision, and verified outcome remain part of one record. That record becomes useful again when the fleet sees a similar condition.
Representative decision recordnode-14 / gpu-03
- 01Fleet evidenceA GPU is operating below its rated link state.
node-14 · gpu-03 · PCIe x4 observed / x16 rated
- 02Harbor recommendationDrain, inspect, reseat, then run targeted verification.
The recommendation includes the evidence, owner, and return criteria.
- 03Operator decisionFleet Ops approves the procedure.
Harbor is non-remediating by default. Your team keeps control.
- 04Verified outcomeThe link is restored and the GPU passes verification.
The resolution becomes fleet memory for future decisions.
The result does not disappear into a closed ticket.It becomes context for the next qualification, diagnosis, and recommendation.
Your fleet's intelligence stays with your fleet.
Harbor runs inside your environment, works with the operation you already have, and keeps operators in control of what happens next.
Customer environmentControl boundary
Infrastructure
KubernetesSlurmBare metal
Harbor reliability modelFleet-specific context stays close to the systems it serves.
Signals in. Recommendations and verification evidence out.
Authorized operators
ReviewApproveExecute
- Deployment
- Self-hosted
- Raw telemetry
- Stays in your environment
- Default action mode
- Recommendation only
- Infrastructure
- Kubernetes, Slurm, and bare metal
Built for teams accountable for GPU capacity.
GPU clouds and neocloudsAI labs and dedicated clustersMulti-provider compute platforms
Start with one recurring fleet decision.
Bring an incident class your team already knows. See how Harbor turns its history, evidence, and operator judgment into a recommendation your team can inspect.