In large AI clusters, GPU hardware faults are among the most common reasons training runs stop. A single failing GPU can halt a job that spans thousands of other GPUs, forcing it to roll back to its last checkpoint and losing hours of compute. Most monitoring tools raise an alert only after the GPU has already failed, when the damage is done.
GPU Failure Prediction is part of Federator.ai Cortex, ProphetStor’s full-stack AI Ops platform for AI factories. It watches the early signs that a GPU is degrading, predicts which GPUs are likely to fail, and acts before they do: moving their workloads to healthy hardware and keeping them out of new allocations until they are repaired.
How It Works
Federator.ai Cortex runs a three-stage pipeline on telemetry collected from every GPU through NVIDIA DCGM:
Detect
Continuously analyzes GPU telemetry for early signs of degradation, such as rising temperatures, accelerating memory errors, and erratic power draw.
Assess
Estimates each GPU’s probability of failure, the most likely failure type, and the expected time remaining, then rolls these into a single health score and priority level.
Act
Moves workloads off GPUs that cross the risk threshold and flags those GPUs so the scheduler no longer assigns new work to them.
Failure Modes Covered
Federator.ai Cortex tracks the failure modes that most often interrupt large-scale AI training, including:
- Thermal degradation: abnormal temperature rise and higher temperatures at the same power draw
- ECC memory errors: accelerating single-bit errors and double-bit error counts
- Power instability: power draw spikes and power-related throttling outside the normal envelope
- Clock degradation: a gradual decline in boost frequency
- NVLink degradation: rising link error rates and reduced bandwidth
- PCIe link errors: increasing replay counts and reduced link width
What Teams Gain
Early Warning, Not Late Alarms
Federator.ai Cortex estimates each GPU’s likelihood of failure and time to failure, so operations teams can act while a GPU is still running instead of after it has taken a job down.
Workloads Moved Before Failure
When a GPU crosses the risk threshold you set, kMotion live-migrates its workloads to healthy hardware. Training continues on new GPUs instead of rolling back to the last checkpoint.
At-Risk GPUs Kept Out of Rotation
A GPU flagged as at risk is removed from the pool of allocatable capacity, so the scheduler stops placing new workloads on it until the GPU is repaired, replaced, or cleared by an operator.
Fleet Health at a Glance
A health score for every GPU, a heatmap by zone, and a fleet-wide view across power, thermal, memory, and interconnect show where risk is building before it becomes downtime.
Frequently Asked Questions
How does Federator.ai Cortex predict GPU failures?
Federator.ai Cortex analyzes GPU telemetry collected through NVIDIA DCGM, looking for early signs of degradation in temperature, memory errors, power delivery, clock speed, and interconnect health. It estimates each GPU’s probability of failure, likely failure type, and expected time to failure.
Which GPU failure types can Federator.ai Cortex detect?
Federator.ai Cortex covers the failure modes that most often interrupt AI training, including thermal degradation, ECC memory errors, power instability, clock degradation, NVLink and PCIe link errors, and XID and firmware errors.
What happens to running workloads when a GPU is predicted to fail?
When a GPU crosses a risk threshold set in advance, Federator.ai Cortex uses kMotion to live-migrate its workloads to healthy GPUs, so training continues without rolling back to the last checkpoint.
Will new jobs be scheduled on a GPU that is predicted to fail?
No. Federator.ai Cortex flags at-risk GPUs and removes them from allocatable capacity, so new workloads are placed only on healthy GPUs until the flagged GPU is repaired or cleared.