GPU data centers run themselves. Starting now.
OneDiagonal is the autonomous operations layer for GPU infrastructure. Our kernel-augmented agents detect hardware failures, recover workloads, optimize utilization, and continuously learn which nodes are safe to schedule on — without human intervention.
GPU data centers are still manually operated.
As GPU fleets scale to thousands of accelerators, the operational complexity grows faster than headcount can. Hardware degrades silently. Jobs hang without explanation. Utilization drops while engineers triage logs. Every hour of downtime costs more than the last.
An autonomous operations layer that sits between your hardware and your workloads. It watches every signal — kernel events, telemetry, collective comms, scheduler state — and acts before failures become outages. No runbooks. No pager rotations. No GPU-hours lost to human reaction time.
Detect. Isolate. Recover. Learn.
Four stages that run continuously across every node in your fleet — closing the loop from raw hardware signal to autonomous action to fleet-wide intelligence.
Continuous Hardware Detection
Autonomous Node Isolation
Workload Recovery
Utilization Optimization
Fleet Intelligence Accumulation
Your data center gets smarter every day.
Every incident, every recovery, every hardware anomaly feeds a continuously updated model of your fleet's health. Over time, OneDiagonal doesn't just react to failures — it prevents them. Degrading nodes are excluded from scheduling before they cause outages. Maintenance is prioritized by risk, not by who got paged last.
gpu-04 flagged by agent at week 10 — before first job failure
Everything ops teams do today. Done autonomously.
- GPU ECC error monitoring
- NIC degradation detection
- Thermal throttle alerts
- PCIe bandwidth anomalies
- NCCL collective stalls
- Autonomous node quarantine
- Workload checkpoint + restore
- Replacement node scheduling
- Blast radius containment
- Zero-touch job recovery
- Continuous health scoring
- Proactive scheduling exclusions
- Utilization gap backfill
- Maintenance queue prioritization
- Cross-job signal correlation
Built for GPU data center operators.
Designed for the engineers who are paged at 3am.
We're working with a small group of GPU data center operators, cloud infrastructure teams, and ML platform engineers. If you're running GPU infrastructure at scale and ops overhead is a growing cost, we want to talk.
Request Early Access
Tell us about your infrastructure. We'll reach out to schedule a technical call.