GPU Observability for AI Infrastructure: Real-time visibility into GPU performance

Why GPU observability matters for AI infrastructure

Traditional infrastructure monitoring can leave critical gaps in GPU-level visibility. CPU, memory, and disk metrics tell you how the server is performing, but they don't always explain what is happening inside the GPU powering an AI workload.

That becomes a problem when everything looks normal at the server level but an AI training job is slower than expected, an inference workload is experiencing performance issues, or a GPU resource is being consumed without an obvious explanation.

Without GPU monitoring, teams can face several operational problems, such as:

• Underutilized GPUs: Expensive compute capacity is sitting idle or is poorly allocated.
• GPU throttling: Performance degrades without enough context to explain why.
• GPU memory pressure: VRAM usage approaches capacity and can affect workload stability.
• Process contention: One workload consumes GPU resources needed by others.
• Hardware health issues: Error correction code (ECC) errors and memory degradation can go unnoticed.
• Reactive troubleshooting: Teams discover GPU problems only after a workload is already affected.

OpManager Nexus closes this visibility gap through continuous, metric-level GPU monitoring. Its Full-Stack Agent provides visibility into GPU performance, health, and resource consumption across every GPU on your monitored servers.

Know if you're getting the performance you pay for

High-end GPUs are a significant infrastructure investment. When that investment is underutilized through poor workload allocation, idle capacity, or a process tying up resources it should not, the cost is real and typically invisible without monitoring.

OpManager Nexus tracks GPU utilization, memory bandwidth utilization, and encoder and decoder utilization continuously. This gives teams the persistent visibility needed to answer the questions that drive better infrastructure decisions:

• Which GPUs across the fleet are consistently running below capacity?
• Which are at or near their limit during peak workload windows?
• Is the constraint on compute utilization, or is memory bandwidth the bottleneck?
• Are encoder and decoder engines in use, or is all the load concentrated on the compute pipeline?

Utilization tracking serves different purposes depending on how your GPU infrastructure is used:

• Shared GPU infrastructure: Where multiple workloads or teams compete for the same devices, utilization data becomes the foundation for fair allocation and informed capacity planning.
• Continuous inference workloads: Persistent utilization tracking reveals the gap between what a GPU is capable of and what it is actually delivering, helping you identify whether compute, memory bandwidth, or another resource is the limiting factor.
• LLM training and inference: GPU performance monitoring (GPM) deep profiling provides granular visibility into the GPU's compute pipeline, enabling deeper performance analysis and optimization.

Find out why performance is dropping

A GPU that is throttling is still running. Utilization metrics may look normal. The workload continues. Yet the GPU has reduced its operating clock speed to stay within thermal or power limits, and performance has dropped, often significantly, with no signal in any standard infrastructure metric.

Throttling is one of the most operationally significant GPU events and one of the hardest to detect without dedicated monitoring. Teams running AI training jobs often discover it only when a run is completed slower than expected, with no explanation available in any log or dashboard they were watching.

OpManager Nexus provides dedicated GPU throttling visibility through:

• Throttle statuses: Know when a GPU is currently operating at a reduced clock speed.
• Throttle reasons: See the specific reasons the GPU clock is being reduced; thermal limits, power limits, and other contributing factors are reported explicitly.
• Clock speed context: Graphics and memory clock speeds, expressed as a percentage of the maximum supported speed, help quantify the impact of throttling.

This changes the troubleshooting question from "Why is my AI workload running slowly?" to "Is the GPU throttling, and what is causing the constraint?" Teams can then investigate the relevant thermal, power, or workload conditions instead of relying on utilization alone.

Prevent memory pressure from disrupting AI workloads

AI models, particularly LLMs and deep learning workloads, consume significant amounts of GPU memory. As workloads scale or models grow, VRAM pressure builds. When it reaches the limit, the result can be a crash, an inference API going down, or a training run terminated with an out of memory error.

The problem with memory pressure is that it builds gradually. OpManager Nexus tracks GPU memory continuously:

• Total, used, and free VRAM: The current state of GPU memory at any point in time
• The memory utilization percentage: How much of the available capacity is consumed, expressed in a form that is directly threshold-configurable for alerting
• Memory bandwidth utilization: How hard the memory controller is being pushed, which can indicate a memory-bound workload even before VRAM capacity becomes the constraint
• Per-process memory consumption: Which specific process is holding how much GPU memory, in megabytes—the data that turns a high-level memory alert into a specific, actionable finding

With memory thresholds configured in OpManager Nexus, teams get alerted when VRAM utilization approaches capacity before an out of memory event affects a workload.

Identify which process is consuming your GPU

GPU utilization is high. Memory is almost full. A workload is slow. Yet none of that tells you which process is responsible.

In shared GPU environments, aggregate metrics are often insufficient for diagnosis. A GPU at 95% memory utilization can indicate different problems, depending on whether that memory belongs to an active training job, a completed job that failed to release its resources, or an unrelated process that should not be on that device at all.

OpManager Nexus tracks GPU resource consumption at the process level:

• The process identity: The process ID, name, command line, and executable path for every process currently using the GPU
• Per-process memory: GPU memory consumption per process, in megabytes
• The process type: Whether the process is a compute workload (such as AI or ML) or a graphics rendering process
• Process counts: Total, compute, and graphics process counts, giving a high-level view of how capacity is divided across workload types

When a process is monopolizing GPU resources, holding memory it should have released, or running on a device it should not be using, process-level monitoring surfaces it directly, helping teams identify the source without manual investigation.

Detect hardware issues before they affect workloads

Not every GPU performance problem comes from the workload. Hardware health and infrastructure conditions can affect reliability and performance.

Monitor GPU ECC memory health

For ECC-capable GPUs, OpManager Nexus tracks both single-bit and double-bit ECC error counts. These are reported since the GPU driver was last loaded and as cumulative lifetime totals.

Single-bit errors are correctable, but a rising count over time is an early warning that memory is under stress. Double-bit errors are uncorrectable and indicate memory that can no longer be trusted to hold data reliably.

Monitoring memory page retirement and row remapping provides additional visibility into potential physical memory degradation before it affects a workload.

Monitor NVLink connectivity

Multi-GPU environments introduce another potential source of performance problems: communication between GPUs. OpManager Nexus monitors active NVLink connections, aggregate transmit (TX) and receive (RX) throughput across all NVLink links on each GPU, and cyclic redundancy check, replay, and recovery error counts. Rising error counts are an early signal of physical issues, such as connector integrity, seating, or thermal stress on the link, that if left unaddressed will destabilize distributed training or multi-GPU inference workloads.

Monitor PCIe performance

PCIe throughput can also affect GPU workloads when data needs to move between the CPU and GPU. OpManager Nexus tracks PCIe TX and RX throughput, generation, and link width, giving teams visibility into bottlenecks on the bus connecting the GPU to the system. PCIe constraints can contribute to inference latency, particularly when data movement between the host and GPU is on the critical path.

Monitor GPU operational modes

OpManager Nexus provides visibility into GPU operational modes, including the Multi-Instance GPU (MIG) mode, compute mode, persistence mode, and display active status.

What GPU metrics should you monitor?

The right GPU monitoring strategy depends on what you need to diagnose. OpManager Nexus brings together multiple layers of GPU telemetry so teams can connect performance changes to the conditions behind them.

These metrics are collected through the Full-Stack Agent and are made available continuously as time series data, giving teams historical context for troubleshooting and capacity planning.

Why ManageEngine OpManager Nexus for enterprise GPU observability

Selecting GPU monitoring software requires evaluating the platform's scalability, security governance, deployment flexibility, and total cost of ownership.

Full-stack enterprise ITOM and infrastructure observability

ManageEngine OpManager Nexus delivers full-stack observability across your infrastructure ecosystem. Rather than requiring separate tools for different components, OpManager Nexus monitors GPU accelerators alongside physical servers, hypervisors, virtual machines, cloud environments, network switches, storage arrays, and application performance within a single management platform.

Flexible deployment for data sovereignty

Enterprises developing proprietary AI models or handling sensitive datasets have different deployment and compliance requirements. Some need to keep infrastructure telemetry within a secure on-premises environment, while others prefer the operational flexibility of the cloud. OpManager Nexus supports both cloud and on-premises deployments, including air-gapped data centers, private clouds, and hybrid environments, helping organizations maintain data sovereignty without compromising infrastructure observability.

Transparent, predictable enterprise pricing

OpManager Nexus eliminates pricing uncertainty with a transparent, predictable, device-based licensing model. Organizations can scale their GPU monitoring capabilities without facing punitive data ingestion fees or unexpected monthly charges.

Deep integration with the ManageEngine ecosystem

OpManager Nexus integrates natively with the broader ManageEngine ITOM suite. From automated ticket creation in ServiceDesk Plus to deep application profiling in Applications Manager, OpManager Nexus connects GPU infrastructure health directly to your established enterprise IT service workflows.

Take control of your enterprise AI infrastructure

AI workloads depend on more than GPU performance. Teams need visibility across the servers and infrastructure powering those workloads to understand where performance issues originate.

OpManager Nexus brings these layers of GPU monitoring together with server observability, giving teams visibility into the GPU, CPU, memory, processes, services, and other critical server metrics from a single platform.