GPU Health Check Guide

A GPU health check is useful when performance, utilisation or reliability has changed and you need evidence before deciding whether to tune, repair or replace equipment.

It is particularly relevant when workloads are slower than expected, GPU use has fallen, or teams disagree about the cause. We compare the GPU with its host and supporting systems so the next action addresses the constraint rather than simply adding or replacing hardware.

The assessment compares GPU activity with host, storage, network and software performance to find where the workload is actually being held up.

A GPU can appear underused because it is waiting for data, CPU resources or another cluster node. We trace the evidence across those layers, then explain which constraint is affecting the workload and what should be investigated or changed first.

Low GPU utilisation can result from workload design, scheduling, host limits or slow data delivery, so utilisation needs to be assessed across the platform.

We compare periods of active GPU computation with the behaviour of the host and data path. This helps distinguish a configuration or scheduling issue from a capacity constraint and shows whether changes to the workload or infrastructure are likely to improve use.

We review GPU temperature, power behaviour and system conditions under load to identify whether thermal or electrical limits are reducing performance.

If limits are being reached, we examine whether the cause sits with cooling, server configuration, power delivery or the component itself. The findings help your team target the right remedy and avoid replacing hardware when an environmental or configuration change may be sufficient.

Yes, storage latency or network delays can leave GPUs waiting, so we examine data delivery and cluster communication alongside accelerator activity.

We look for idle periods that coincide with data access or communication between nodes, then trace those delays through storage and the interconnect. This helps establish whether improving the data path could deliver more value than adding GPUs.

We compare driver, firmware, CUDA and supporting software versions across systems to identify compatibility issues and inconsistencies linked to instability.

Differences between otherwise similar servers can help explain why faults are intermittent or isolated to particular workloads. We use the findings to distinguish software or versioning problems from hardware faults and establish a more consistent baseline for further troubleshooting.

Yes, the findings can separate issues that may be tuned from hardware faults, capacity limits and lifecycle risks that may justify repair or replacement.

The recommendation is based on the evidence gathered about configuration, workload behaviour and component condition. We explain what can remain in service, what needs remedial work and where replacement should be considered, giving your team a basis for prioritising spend and timing.

Most checks use telemetry, logs and read-only system information, so GPU workloads can usually continue while we assess the environment.

If stress testing or deeper diagnostics could affect production jobs, we agree the method, timing and safeguards with your team first. The assessment plan makes clear what information is needed and which activities, if any, require a maintenance window.

You will have an evidence-based view of the main performance or reliability issue, its likely cause and the actions worth prioritising.

We present the findings in terms of operational impact and practical next steps, such as tuning, remediation, capacity changes or refresh planning. This gives IT and the business a clearer basis for assigning work, setting timing and preparing a budget.

Yes, we can help turn the agreed findings into a plan and support the sourcing, implementation and ongoing optimisation of suitable GPU infrastructure.

We can work with your team to resolve configuration issues, plan hardware changes or assess the wider AI environment, keeping follow-on decisions tied to assessment evidence and supporting the platform through its lifecycle. Discuss the findings with our team.