AI Infrastructure Observability for GPU Clusters
AI infrastructure observability is the practice of correlating GPU, network, storage, power and temperature data to explain why a training or inference workload is slow, not just that it is.
Sensaka brings GPU, container, node, network, storage, power, temperature, liquid cooling and hardware health data into one operational view. Instead of confirming only that GPU utilization is low, infrastructure teams can investigate the conditions around the workload and identify whether the likely constraint comes from compute, communication, data delivery or the physical node.
Low GPU Utilization Is Usually a Symptom
A GPU can be allocated and online while producing little useful work. The workload may be waiting for data, blocked by network communication, affected by storage latency, competing with another container or running on a node with temperature, power or hardware health problems.
When each domain is monitored in a separate tool, teams see different fragments of the same event. GPU administrators inspect accelerator metrics. Network teams review RoCE or RDMA counters. Storage teams check throughput and latency. Data center teams look at power and cooling. Application teams examine workload logs.
Sensaka aligns these signals so teams can investigate the complete infrastructure path behind an AI workload.
One Timeline Across Compute, Network and Storage
Sensaka correlates operational metrics from the main infrastructure domains that affect AI workload performance.
GPU and Node Visibility
Track accelerator utilization, memory usage, temperature, power, ECC conditions and node health. View performance by accelerator model, node, cluster or project to identify where utilization patterns differ.
Container and Workload Context
Connect workloads and containers to the GPUs they use. Follow the relationship from a task to its container, GPU, physical node, network port and storage dependency. This context helps separate a busy accelerator from an overallocated or underused one.
High Speed Network Observability
Monitor RoCE and RDMA traffic conditions, including packet loss, retransmission, latency and port health. Network indicators can be examined beside GPU utilization to determine whether communication waits are limiting distributed training.
Storage and Data Delivery Observability
Analyze throughput, IOPS, read and write latency, data loading time and checkpoint write time. These signals help determine whether GPUs are waiting because training data is not being delivered quickly enough.
Physical Infrastructure Health
Combine in band performance data with out of band hardware information from BMC interfaces. Temperature, power, ECC and component health remain important when an operating system or workload view cannot explain the problem.
From Metrics to an Investigation Path
Sensaka organizes AI infrastructure observability into four connected stages.
Monitor
Collect GPU and node status, network loss and latency, storage performance, power, temperature, liquid cooling and hardware health data.
Correlate
Connect tasks to containers, containers to GPUs, GPUs to physical nodes, and nodes to network and storage resources. Associate the affected workload with its project and service context.
Diagnose
Compare time aligned signals to identify evidence of a compute constraint, network communication problem, storage supply bottleneck or node hardware condition. The platform presents the related indicators and the likely impact scope so operators can make an informed decision.
Optimize
Turn the diagnosis into an operational recommendation, such as reviewing workload priority, reallocating GPU resources, isolating a faulty node, restarting an affected task or adjusting a queue and scheduling policy. Execution can be placed behind approval and recorded for audit.
Investigate Common AI Infrastructure Problems
GPU Utilization Drops During Training
Place GPU utilization, network latency and storage wait on the same timeline. Review data loading and communication waits during the affected period, then determine which infrastructure domain shows the strongest evidence of a bottleneck.
Distributed Training Slows Down
Review RoCE or RDMA loss, retransmission and port health together with GPU activity. Identify whether a communication issue is affecting one node, one port or a larger section of the cluster.
Checkpoint Writes Take Too Long
Compare storage throughput, IOPS and write latency with workload checkpoint timing. Determine whether storage performance is delaying the workload and leaving accelerators idle.
A Container Uses Less GPU Than Requested
Compare requested resources with actual usage over time. Identify workloads that hold expensive capacity while producing little activity, then review whether resource specifications or scheduling rules should change.
A Node Appears Healthy in Kubernetes but Workloads Fail
Use out of band hardware monitoring to review temperature, power, ECC and component conditions independently of the operating system. This provides another evidence source when software level monitoring does not reveal the fault.
How DCIM Requirements Change for AI and GPU Clusters
A data center infrastructure management model that works well for conventional server rooms usually needs to be extended before it is useful for an AI cluster. The disciplines are the same. The density, the rate of change and the scope of a single fault are not.
| Requirement | Conventional servers | AI and GPU clusters |
|---|---|---|
| Power per rack | A conventional server rack is planned around a stable power envelope, and rack level power is usually reviewed periodically rather than continuously. | An AI rack concentrates far more power in the same footprint and the draw changes with the job. Power data needs to be collected at rack and circuit level frequently enough to show the step changes that workloads create. |
| Cooling | Air cooling with room and aisle temperature monitoring is normally sufficient, and the room average is a reasonable proxy for what each server experiences. | Dense accelerator racks often combine air and liquid cooling. Coolant supply and return temperature, pressure differential, flow indicators and leak events become operational data, and a room average can hide a rack that is already in trouble. |
| Capacity model | Free rack units are a usable measure of remaining capacity in most conventional rooms. | Rack units, power headroom and cooling capacity have to be evaluated together. A rack can have space available while power or thermal conditions prevent anything else being installed in it. |
| Asset record | A server, its location, its configuration and its warranty status describe the asset adequately. | The record also needs the accelerators inside the chassis, their health, the cooling path that serves them and the workloads and tenants that depend on them. |
| Fault signature | Component faults such as a failed disk, a failed power supply or a memory error are usually local to one machine. | A single degraded node can stall a distributed training job across many nodes, so the operational question is which workloads were affected rather than which server failed. |
| Collection interval | Polling intervals designed for steady state workloads are generally adequate. | Load arrives and disappears in steps as jobs start and stop. Intervals suited to a steady state estate can miss the transient thermal and power conditions that matter most. |
| Correlation scope | Facility data and IT monitoring are often governed by separate teams and separate tools without much friction. | Compute, network, storage and facility data have to be read on one timeline, because the explanation for a performance change frequently lives in a different domain from the symptom. |
The practical consequence is that DCIM for an AI cluster cannot stay a facility discipline. Rack power, cooling state and physical capacity have to be readable next to accelerator health and workload context, which is the correlation model this page describes.
Built for Cross Team Operations
AI workload performance spans several technical domains. Sensaka gives GPU, platform, network, storage and data center teams a common investigation view.
Teams can work from the same event timeline, the same infrastructure relationships and the same affected workload context. This reduces repeated data collection and helps each team understand how its part of the infrastructure contributes to the final workload outcome.
Everything the Correlation Model Needs
Observability That Extends Into the Physical Layer
Many observability platforms begin above the operating system. Sensaka extends the operational view into the physical AI infrastructure layer. The platform combines software metrics with multi vendor server, accelerator, network, storage, power, cooling and BMC data. This helps operators see hardware and facility conditions that may remain invisible to Kubernetes or application monitoring alone.
Sensaka also connects observability to infrastructure relationships, workload context and operational workflows. The goal is a practical investigation path from a performance symptom to the affected resources, likely cause and next action.
Frequently Asked Questions
What is AI infrastructure observability?
AI infrastructure observability is the ability to understand how GPUs, containers, nodes, networks, storage and physical infrastructure conditions affect AI training and inference workloads. It requires correlation across these domains rather than isolated monitoring dashboards.
How is this different from GPU monitoring?
GPU monitoring focuses primarily on accelerator metrics. Sensaka adds workload relationships, network communication, storage data delivery, node hardware health, power, temperature and cooling context to help explain why GPU performance changes.
Does Sensaka monitor RoCE and RDMA networks?
Sensaka supports the collection and analysis of network conditions such as loss, retransmission, latency and port health. The exact indicators and integration method depend on the network equipment and telemetry interfaces available in the customer environment.
Can Sensaka identify a root cause automatically?
Sensaka correlates evidence, highlights likely bottleneck domains and provides an investigation and recommendation path. Final diagnosis and any operational action should follow the customer's approval, change and audit requirements.
Can hardware still be monitored when the operating system is unavailable?
Where supported by the server, Sensaka can collect hardware information through out of band management interfaces such as Redfish and IPMI. This monitoring path operates independently of the production operating system.
Which environments are suitable for this solution?
The solution is designed for enterprise AI data centers, public computing centers, research clusters and multi tenant GPU environments that need a shared operational view across compute, network, storage and physical infrastructure.
Find the Real Cause of GPU Performance Problems
Move from isolated utilization charts to a connected view of the infrastructure behind every workload.
Related: GPU Usage Metering, AI Infrastructure CMDB, Liquid Cooling Monitoring