Rognix watches every GPU, every rack, every training job. It predicts thermal runaway hours before sensors trip, surfaces underutilized accelerators wasting power, and acts on cooling imbalances before workloads fail. AI-native operations, not retrofitted server monitoring.
Standard servers? Self-serve free monitoring →
Drop the collector on every GPU node, every rack-top switch, every BMC-addressable host. NVIDIA-SMI / Slurm / Kubernetes / Ray hooks bring topology and workload context with zero re-instrumentation.
Continuous baselines per GPU, per rack, per workload. Thermal trends, cooling efficiency, NVLink fabric health, power draw vs. inference throughput, all explained in plain English with confidence scores.
Manual recommendations, assisted approval queues, or autonomous handling for low-risk routine work, load-shed a thermally stressed rack, scale down idle GPU pools, fail over a degraded NVLink before a job dies.
Static thresholds don't survive contact with a 1,024-GPU training run. Rognix builds continuous baselines per device and per workload, fuses signals across racks, and explains every recommendation with the underlying evidence.
North-row inlet temps climbing 4.2°C over 30m while south-row holds. Cooling imbalance, likely a CRAC unit derating. Rebalance inference traffic to rack-7 before throttling lands.
Slurm queue has 11 pending jobs eligible for this device. Likely a misconfigured node selector. Estimated waste: $118 in idle GPU-hours so far this period.
Pattern matches 9 historical PSU failures across our corpus. Live training job at 47% epoch, schedule migration to dgx-a100-09 within 2h to avoid checkpoint loss.
Per-device utilization, memory pressure, temperatures, power draw, NVLink/PCIe error counts. NVIDIA, AMD MI-series, and Intel Gaudi.
Rack-level inlet/outlet baselines, CRAC unit health, hot-spot detection, PUE trending, predicted throttle events before sensors trip.
Pattern-match against historical PSU, NVLink, HBM, and disk failure modes. Migrate training jobs before checkpoint loss.
Surface idle accelerators, misrouted batches, and stranded reservations. Quantify wasted GPU-hours and recover capacity for queued jobs.
NVLink, InfiniBand, RoCE topology, link error counts, bandwidth degradation, congestion patterns affecting all-reduce and pipeline ops.
Live power draw vs. budget, redistribution suggestions, efficiency scoring per workload, identification of stranded capacity.
Rognix recommends. You execute. Full context, zero automation. Good for the first weeks of any AI deployment review.
Drafts an action plan with a diff-style preview, "shed this rack, migrate these 4 jobs". You approve. SLA timers stop stale approvals from blocking incidents.
Low-risk routine actions handled automatically, workload rebalance ahead of cooling events, idle-GPU shutdown, log rotation. High-risk routes to your approval queue.
"Rognix flagged a cooling imbalance two hours before our DCIM did. We migrated the training run; the rack throttled at the predicted time. Quietly saved a week of compute."
"It found 11% of our H100 fleet running idle from a stale node selector, wasted GPU-hours we wouldn't have caught for weeks. Paid for the year in the first month."
"The plain-English reasoning means our junior on-call engineers can act on GPU thermal events without escalating. MTTR dropped 60% in the first quarter."
A 30-minute deployment review. We connect to a sample rack of your fleet (read-only) and ship you a written assessment of three opportunities, thermal, utilization, and workload, within 5 days.