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This observation would help verify a different incident: GPU health tests fail because the driver package on a server was installed from an untrusted repository, and management cannot reproduce its package dependencies. It does not directly establish the required result for the current case: A known CUDA sample and `nvidia-smi` both run successfully on the approved kernel and the remaining fleet retains known-good operation.

This observation would help verify a different incident: A BlueField DPU application fails after the host updates DOCA packages, while the DPU firmware and embedded software versions remain from an earlier release. It does not directly establish the required result for the current case: A known CUDA sample and `nvidia-smi` both run successfully on the approved kernel and the remaining fleet retains known-good operation.

This observation would help verify a different incident: Base Command Manager is installed on a management node but the cluster database is stored only on a single local disk, and leadership claims the control plane is highly available. It does not directly establish the required result for the current case: A known CUDA sample and `nvidia-smi` both run successfully on the approved kernel and the remaining fleet retains known-good operation.

This underlying cause is associated with another GPU platform incident: An operator removes the NVIDIA driver while GPU containers are still running, leaving orphaned jobs and an inconsistent module state across the node. The observed case instead concerns: GPU driver releases have compatibility requirements that an otherwise healthy Linux boot does not guarantee.

This underlying cause is associated with another GPU platform incident: A BlueField DPU application fails after the host updates DOCA packages, while the DPU firmware and embedded software versions remain from an earlier release. The observed case instead concerns: GPU driver releases have compatibility requirements that an otherwise healthy Linux boot does not guarantee.

This underlying cause is associated with another GPU platform incident: A container sees its host GPU but fails when run on a different node whose driver is older than the image's required CUDA feature level. The observed case instead concerns: GPU driver releases have compatibility requirements that an otherwise healthy Linux boot does not guarantee.

This prevention control targets another operational risk: A security patch changes the host kernel and Secure Boot settings, and the NVIDIA GPU module stops loading despite the device remaining visible on PCIe. The currently reported mechanism remains: GPU driver releases have compatibility requirements that an otherwise healthy Linux boot does not guarantee.

This prevention control targets another operational risk: A BlueField DPU application fails after the host updates DOCA packages, while the DPU firmware and embedded software versions remain from an earlier release. The currently reported mechanism remains: GPU driver releases have compatibility requirements that an otherwise healthy Linux boot does not guarantee.

This prevention control targets another operational risk: A BCM provisioning environment discovers every compute node, yet new GPU hosts join the wrong category and receive CPU-only operating system images. The currently reported mechanism remains: GPU driver releases have compatibility requirements that an otherwise healthy Linux boot does not guarantee.

This option could be appropriate for a different NVIDIA infrastructure problem (3.4: Install, update, and remove NVIDIA GPU and DOCA drivers): GPU health tests fail because the driver package on a server was installed from an untrusted repository, and management cannot reproduce its package dependencies. The present issue is more specifically caused by: Kernel module authentication can block driver initialization even when the physical accelerator is present and healthy.

This option could be appropriate for a different NVIDIA infrastructure problem (3.4: Install, update, and remove NVIDIA GPU and DOCA drivers): An operator removes the NVIDIA driver while GPU containers are still running, leaving orphaned jobs and an inconsistent module state across the node. The present issue is more specifically caused by: Kernel module authentication can block driver initialization even when the physical accelerator is present and healthy.

This option could be appropriate for a different NVIDIA infrastructure problem (3.3: Install clusters, categories, interfaces, Slurm, Enroot, and Pyxis): A new node is added to the wrong Slurm partition and gains access to restricted customer workloads despite being intended for untrusted experimentation. The present issue is more specifically caused by: Kernel module authentication can block driver initialization even when the physical accelerator is present and healthy.

This observation would help verify a different incident: An operator removes the NVIDIA driver while GPU containers are still running, leaving orphaned jobs and an inconsistent module state across the node. It does not directly establish the required result for the current case: The signed module loads under the enforced boot policy and a CUDA smoke test completes without reducing platform integrity controls.

This observation would help verify a different incident: A BlueField DPU application fails after the host updates DOCA packages, while the DPU firmware and embedded software versions remain from an earlier release. It does not directly establish the required result for the current case: The signed module loads under the enforced boot policy and a CUDA smoke test completes without reducing platform integrity controls.

This observation would help verify a different incident: A training cluster stages a container with Enroot, but Slurm launches it without the intended Pyxis integration and the job cannot access accelerators. It does not directly establish the required result for the current case: The signed module loads under the enforced boot policy and a CUDA smoke test completes without reducing platform integrity controls.

This underlying cause is associated with another GPU platform incident: A new NVIDIA driver is installed on every host without checking its supported CUDA runtime and kernel module compatibility, leading to failed CUDA initialization. The observed case instead concerns: Kernel module authentication can block driver initialization even when the physical accelerator is present and healthy.

This underlying cause is associated with another GPU platform incident: An operator removes the NVIDIA driver while GPU containers are still running, leaving orphaned jobs and an inconsistent module state across the node. The observed case instead concerns: Kernel module authentication can block driver initialization even when the physical accelerator is present and healthy.

This underlying cause is associated with another GPU platform incident: Base Command Manager is installed on a management node but the cluster database is stored only on a single local disk, and leadership claims the control plane is highly available. The observed case instead concerns: Kernel module authentication can block driver initialization even when the physical accelerator is present and healthy.

This prevention control targets another operational risk: A BlueField DPU application fails after the host updates DOCA packages, while the DPU firmware and embedded software versions remain from an earlier release. The currently reported mechanism remains: Kernel module authentication can block driver initialization even when the physical accelerator is present and healthy.

This prevention control targets another operational risk: An operator removes the NVIDIA driver while GPU containers are still running, leaving orphaned jobs and an inconsistent module state across the node. The currently reported mechanism remains: Kernel module authentication can block driver initialization even when the physical accelerator is present and healthy.

This prevention control targets another operational risk: A container in a multi-tenant GPU cluster can see accelerators allocated to another job because the runtime launches with unrestricted device exposure. The currently reported mechanism remains: Kernel module authentication can block driver initialization even when the physical accelerator is present and healthy.

This option could be appropriate for a different NVIDIA infrastructure problem (3.4: Install, update, and remove NVIDIA GPU and DOCA drivers): A new NVIDIA driver is installed on every host without checking its supported CUDA runtime and kernel module compatibility, leading to failed CUDA initialization. The present issue is more specifically caused by: Host and DPU components form a coupled software stack; isolated upgrades can break offload services.

This option could be appropriate for a different NVIDIA infrastructure problem (3.4: Install, update, and remove NVIDIA GPU and DOCA drivers): GPU health tests fail because the driver package on a server was installed from an untrusted repository, and management cannot reproduce its package dependencies. The present issue is more specifically caused by: Host and DPU components form a coupled software stack; isolated upgrades can break offload services.

This option could be appropriate for a different NVIDIA infrastructure problem (3.5: Install the NVIDIA Container Toolkit): A Docker AI image launches on a GPU host but reports no CUDA devices because the NVIDIA container runtime integration was never installed. The present issue is more specifically caused by: Host and DPU components form a coupled software stack; isolated upgrades can break offload services.

This observation would help verify a different incident: An operator removes the NVIDIA driver while GPU containers are still running, leaving orphaned jobs and an inconsistent module state across the node. It does not directly establish the required result for the current case: A supported DOCA sample completes end-to-end and both host and DPU report the approved paired software baseline.

This observation would help verify a different incident: GPU health tests fail because the driver package on a server was installed from an untrusted repository, and management cannot reproduce its package dependencies. It does not directly establish the required result for the current case: A supported DOCA sample completes end-to-end and both host and DPU report the approved paired software baseline.

This observation would help verify a different incident: A fleet of GPU hosts uses manually customized operating system packages, making a failed driver upgrade impossible to reproduce or roll back consistently. It does not directly establish the required result for the current case: A supported DOCA sample completes end-to-end and both host and DPU report the approved paired software baseline.

This underlying cause is associated with another GPU platform incident: A new NVIDIA driver is installed on every host without checking its supported CUDA runtime and kernel module compatibility, leading to failed CUDA initialization. The observed case instead concerns: Host and DPU components form a coupled software stack; isolated upgrades can break offload services.

This underlying cause is associated with another GPU platform incident: A security patch changes the host kernel and Secure Boot settings, and the NVIDIA GPU module stops loading despite the device remaining visible on PCIe. The observed case instead concerns: Host and DPU components form a coupled software stack; isolated upgrades can break offload services.

This underlying cause is associated with another GPU platform incident: A Docker AI image launches on a GPU host but reports no CUDA devices because the NVIDIA container runtime integration was never installed. The observed case instead concerns: Host and DPU components form a coupled software stack; isolated upgrades can break offload services.

This prevention control targets another operational risk: An operator removes the NVIDIA driver while GPU containers are still running, leaving orphaned jobs and an inconsistent module state across the node. The currently reported mechanism remains: Host and DPU components form a coupled software stack; isolated upgrades can break offload services.

This prevention control targets another operational risk: A security patch changes the host kernel and Secure Boot settings, and the NVIDIA GPU module stops loading despite the device remaining visible on PCIe. The currently reported mechanism remains: Host and DPU components form a coupled software stack; isolated upgrades can break offload services.

This prevention control targets another operational risk: An AI job runs on one compute node, but Slurm cannot distribute it because GPU resources are absent from the scheduler's generic resource definitions. The currently reported mechanism remains: Host and DPU components form a coupled software stack; isolated upgrades can break offload services.