Productive utilization
Separate GPUs doing model work from GPUs that are idle, stalled, or busy-waiting.
For neoclouds
Turn customer GPU-hours into productive training
RidgeScope shows where allocated GPU time stops producing useful work, explains whether the workload or infrastructure is responsible, and gives support teams the evidence to act.
What changes for your cloud01 02 03 04
An AI-native efficiency layer above the infrastructure
Job vs. infrastructure
Show whether the customer workload, cluster fabric, scheduler, node, or GPU caused the incident.
Faster tenant support
Give support and SRE teams one cited investigation instead of a cross-team log search.
Evidence for every SLA
Name the affected rank, node, GPU, interval, and signal behind every conclusion.
From fleet signal to owner
Find waste, prove the cause, route the action
Measure
Mission Control shows allocated, computing, waiting, idle, and unhealthy GPUs across the fleet.
Prioritize
Rank non-productive time by workload, tenant, cost exposure, and failure mode.
Explain
Correlate Slurm, GPU, CUDA, NCCL, rank logs, and training progress into one verdict.
Act
Route the next step to the customer, platform, network, scheduler, or hardware owner.
RidgeScope diagnoses and recommends. It does not automatically reschedule jobs, reclaim GPUs, or modify customer training code.
Training-aware evidence94% engine · 7% SM 1 slow rank · 15 waiting 4.9 GPU-hours
Cases infrastructure utilization alone cannot explain
Busy did not mean productive
A slow interconnect kept collectives moving while GPUs spent most of the run waiting instead of computing.
The fleet average hid the cause
Per-rank evidence localized one straggler that set the pace for the entire synchronous allocation.
An idle job held the allocation
Memory was resident, no kernels launched, and logs were empty. RidgeScope still identified the waste.
