Semantic intelligence
Every vendor, one language
Each GPU, PSU, fan and coolant loop becomes a typed resource in one adaptive graph, whatever protocol it speaks.
Hot-reloaded OEM profiles · OpenRMC compatible
ARAM · Autonomous Rack Management
Self-healing infrastructure for GPU-native data centers
ARAM detects faults in under a second, finds the root cause in seconds, and remediates within the policies you set, across every vendor in the rack.
The problem
At frontier scale, failures outpace the people diagnosing them
In a GPU rack, one small fault tips the next. ARAM stops the chain where it starts.
okwaiting for telemetry…
Example scenario.
18.7%1
of GPU time is lost to failures
14 min2
mean time to failure on a 131,072-GPU cluster
90 min3
to diagnose a single fault by hand
1 day+4
to resolve over a third of Azure GPU failures
How it works
The autonomous brain for GPU racks
ARAM senses, reasons, acts and keeps improving across every vendor component in the rack.
01 · Detect
Sub-second detection
Faults and anomalies from real-time telemetry, as they start.
02 · Diagnose
Root cause in seconds
Traced across GPU, cooling, power and network, not just the symptom.
03 · Heal
Remediate within policy
Isolate, fix and verify, automatically where your policies allow.
04 · Predict
Prevent the next one
Learn failure patterns and act before costly disruption.
What that looks like on a live rack
A memory fault on one GPU is caught as it starts, isolated by policy, and verified clean before the node rejoins the fleet.
rack-a14 · 8× GPU nodes2 warn · 1 isolated
02:14:07warngpu5 xid=48 dbe ecc rising
02:14:08critpolicy gpu-mem-fault: isolate gpu5
02:16:02okverify 3/3 clean · gpu5 restored
For operators and SREs
Today's tools weren't built for AI fleets
Operators stitch together vendor tools and human judgment. ARAM replaces the stitching.
Today
With ARAM
Non-standard, heterogeneous fleet
One semantic layer across vendors and protocols
Vendor-specific tools
A single control plane for the whole rack
High-latency detection
Sub-second fault and anomaly detection
Human intervention to resolve
Autonomous remediation within your policies
Vendor protocols
NVIDIA DCGMAMD ROCmDell iDRACHPE iLOSONiCModbus CDU
ARAM semantic graph
GPUPSUFanNVLinkMemoryCoolantSwitchLeak
One Redfish-compliant API
02:14:07.312warngpu5 xid=48 dbe ecc 3 → 7 in 40s
02:14:07.640warndiagnose cause=hbm degradation conf 0.94
02:14:08.105critpolicy gpu-mem-fault isolate gpu5 drain
02:14:31.870okremediate gpu reset ok
02:16:02.004okverify 3/3 clean passes restored
AI for AI
Models built for rack failures
Purpose-built models detect, predict, root-cause and remediate failures in AI rack components.
GPU · PSU · DIMM · fan · NVLink · thermal · coolant
Heal
Fixes that are verified
Runbooks act within your policies, then confirm with consecutive clean readings, or escalate.
GPU reset · power cap · workload drain · pump stop
Audit
Every action on the record
Each detection, decision and action is logged with the evidence and the policy that allowed it.
Who we serve
For the teams running GPU fleets
Direct to GPU operators, and through partners who already own the rack.
NeoCloudsSovereign cloudsEnterprisesSystem integratorsDCIM vendorsSilicon vendorsODMs
Recover lost GPU time
Shorter time from fault to fix means more of the fleet doing the work you bought it for.
Hours become seconds
Diagnosis that takes an engineer 90 minutes becomes a root cause in seconds.
Autonomy at your pace
Start with detection and recommendations. Approve actions. Then let policies run the fixes you trust.
Pilot
See what ARAM does on your racks
- Racks you choose, vendors you already run
- Your policies decide what ARAM may fix on its own
- Planned with an ARAM engineer, not a sales sequence
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