GPU Memory Mismatch – Troubleshooting Guide

GPU Memory Mismatch – Troubleshooting Guide

Alert Information

Alert Name: GPU Memory Mismatch

Severity: P3 – Medium Impact

Alert Description:
This alert is generated when the detected GPU memory size on a node does not match the expected configuration.

Alert Summary

The monitoring system has detected that the GPU memory reported by the system differs from the expected GPU memory configuration.


A mismatch may indicate:

  • Incorrect GPU SKU installed

  • GPU firmware issue

  • Hardware fault

  • Improperly seated GPU

  • BIOS configuration mismatch

Impact

  1. GPU workloads may fail or run incorrectly.
  2. Resource allocation in the HPC cluster may become inconsistent.
  3. Indicates possible hardware misconfiguration or GPU malfunction.

Alert Handling Procedure

L1 Engineer Actions

When this alert is triggered, the L1 engineer must perform the following steps:
  1. Review and log the alert summary and node details.

  2. Wait for 20 - 30 Mints if it gets auto resolved. 

  3. Create an internal ticket for tracking the issue.

  4. Verify if the alert is persistent or intermittent.

  5. If the alert continues to appear, escalate the issue to L2 engineering.

  6. After confirmation from L2, update the Issues Master Sheet with the incident details.


L2 Engineer Investigation Steps

L2 engineers must perform deeper diagnostics to validate the GPU configuration.

Step 1 – Check GPU Details

Run the command:
nvidia-smi

Verify:

  • Number of GPUs detected

  • GPU model

  • GPU memory per device


Step 2 – Verify System Memory

Run:
cat /proc/meminfo

This provides system memory information and helps verify node memory configuration.

Step 3 – Check Hardware Memory Details

Run:
sudo dmidecode -t memory

This command displays hardware memory configuration and installed memory modules.

Step 4 – Validate GPU Configuration

Compare the collected information and verify:
  • Installed GPU SKU matches expected configuration

  • No GPU is reporting reduced memory size


Step 5 – Check Firmware and BIOS

Verify the following:
  • GPU firmware version

  • Node BIOS version

  • Hardware configuration consistency with cluster standards

Step 6 – Hardware Troubleshooting

If a mismatch is detected:
  1. Perform a power cycle of the node.
  2. Post customer confirmation, Reseat the GPU according to OEM hardware handling guidelines.
  3. Boot the system and re-run diagnostic commands.

Step 7 – OEM Escalation

If the issue persists after troubleshooting:
  • Escalate the issue to the OEM/vendor support team.
  • Provide:
    • Command outputs
    • Node details
    • GPU information
    • Logs from diagnostics

Command Reference

Verify GPU Visibility on Node:
nvidia-smi
Validate System Memory Configuration:
cat /proc/meminfo

Verify Installed Memory Modules:
sudo dmidecode -t memory

This command verifies:
  1. Installed memory modules
  2. Slot population
  3. Memory capacity
  4. Manufacturer details

Remarks

  1. In some scenarios, GPUs may appear healthy at the OS level, but Kubernetes does not advertise them as allocatable resources.
  2. In such cases, GPU workloads will not be scheduled on the node, even though nvidia-smi shows GPUs as available.
  3. Monitoring systems relying only on nvidia-smi may fail to trigger alerts. Therefore, GPU availability must also be validated from the Kubernetes scheduler perspective.


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