Select Two (Multi-Select) Questions

Increasing the CUDA_VISIBLE_DEVICES environment variable.

Why it fails: CUDA_VISIBLE_DEVICES restricts which GPUs a process can access; it does not enable resource sharing. Setting this variable only filters the visible devices. It cannot partition a single GPU or allow multiple containers to access the same physical hardware concurrently for parallel processing in an isolated manner.

Setting a higher GPU limit in the Kubernetes manifest.

Why it fails: Increasing the GPU limit only allocates more resources to a single container. It does not facilitate sharing or partitioning of a physical GPU. In fact, requesting a higher limit typically consumes the entire GPU, preventing other containers from utilizing the device, which is the opposite of the requested goal.

Deploying the NVIDIA Network Operator.

Why it fails: The Network Operator focuses on high-speed interconnects like InfiniBand and RoCE, managing network resources and performance. It does not provide functionality for GPU resource partitioning or sharing. Networking and compute resource management are distinct domains in cluster architecture, and this component does not facilitate the sharing of GPU hardware.

Increasing the size of the swap partition on each node

Why it fails: Swap space is detrimental to high-performance AI training. If the system starts swapping to disk, the latency penalty is severe and will likely lead to timeout errors in distributed training frameworks. High-performance workloads should be configured to fit entirely within the available physical RAM and GPU memory.

Disabling the NVIDIA persistence mode to save power

Why it fails: Disabling persistence mode forces the driver to re-initialize the GPU state upon every process launch. This creates unnecessary overhead and latency, especially in environments with many short-lived tasks or frequent job scheduling. Maintaining persistence is critical for stable performance in production AI infrastructure environments.

Reducing the number of CUDA streams per training job

Why it fails: CUDA streams are used to overlap compute and data movement. Reducing the number of streams typically lowers hardware utilization and prevents the overlapping of tasks, which negatively impacts training efficiency. Ideally, developers should maximize concurrency to keep the GPU streaming multiprocessors fully occupied with work.

NVIDIA Data Center GPU Manager (DCGM)

Why it fails: While DCGM is a valuable tool for monitoring and managing GPU health, it is not strictly required for a container to simply run a workload on a GPU. A container can function perfectly well without DCGM installed, provided the driver and device plugin are correctly configured and operational.

Pre-installed CUDA Toolkit inside the container

Why it fails: The CUDA toolkit is an application-level dependency. While it is needed for building applications, it is not required for the infrastructure to support GPU workloads. In many production environments, the runtime libraries are dynamically mounted or provided through a base image, making a full pre-installed toolkit unnecessary for general execution.

Increase the number of threads in the PyTorch DataLoader.

Why it fails: Adjusting DataLoader threads affects CPU-to-GPU data transfer within a single node, not the communication between nodes. While important for local performance, it does not address network-level latency, bandwidth saturation, or inter-node configuration errors that characterize distributed training performance bottlenecks in high-performance computing clusters.

Switch from NCCL to MPI for all communication operations.

Why it fails: NCCL is highly optimized specifically for NVIDIA GPUs, leveraging features like NVLink and InfiniBand RDMA that standard MPI implementations may not utilize as effectively. Switching to generic MPI usually results in decreased performance rather than an improvement, as it loses the proprietary optimizations tailored for NVIDIA hardware.

Lower the precision of the model to float16.

Why it fails: Lowering precision affects compute intensity and memory usage, but it does not resolve network-level communication issues. While it might speed up individual node operations, it does not provide the diagnostic data needed to identify why multi-node synchronization is failing or underperforming in the network fabric.

Significant increase in raw GPU compute performance.

Why it fails: Containers introduce virtually zero overhead compared to bare-metal performance, but they do not increase the raw compute capability of the GPU itself. A model running in a container will perform at the same hardware speed as one running on bare metal, provided there is no resource contention.

Automatic elimination of GPU driver compatibility issues.

Why it fails: Containers rely on the host's NVIDIA driver to interface with the GPU hardware. While they provide isolation for user-space libraries, they do not resolve base driver version issues. If the host driver is incompatible with the container's requirements, the workload will still fail to launch correctly.

Direct access to the GPU firmware for kernel customization.

Why it fails: Containers do not provide access to low-level GPU firmware or host kernel structures for security reasons. Any customization of hardware behavior must be performed at the host level by the system administrator, as containers are intentionally sandboxed to prevent them from interfering with critical hardware control layers.

Ensure that the private registry supports manifest lists for multi-architecture images.

Why it fails: While manifest lists can be important for multi-architecture clusters, they are not a strict requirement for air-gapped deployment. The primary requirements are mirroring images and configuring the Operator to use the private registry. The registry must simply be able to serve the images; support for manifest lists is not a mandatory action for this scenario.

Disable the driver container and install drivers manually on each node.

Why it fails: Disabling the driver container is not required for air-gapped deployments; the driver container can be used if its image is mirrored. Manual driver installation would defeat the purpose of using the Operator and is not a necessary action. The Operator can manage drivers in air-gapped environments as long as the driver image is available in the private registry.

Set the environment variable 'AIRGAP=true' in the operator's deployment.

Why it fails: There is no 'AIRGAP=true' environment variable for the NVIDIA GPU Operator. Air-gapped support is achieved through configuration of image registries and mirroring, not by a simple environment variable. This option is a fabricated setting and would not have any effect on the deployment, making it incorrect.

Install the NVIDIA GPU Operator using the --offline flag.

Why it fails: There is no --offline flag for the NVIDIA GPU Operator Helm chart or installation. The Operator does not have a built-in offline mode. Air-gapped deployments are handled by mirroring images and configuring the registry endpoints. Relying on a non-existent flag would cause installation failure. Therefore, this is not a valid action for the scenario.

Disable the GPU Operator's driver management and install the driver manually on each node.

Why it fails: While it is possible to disable driver management, it is not a required action for air-gapped deployment. The GPU Operator can manage the driver in an air-gapped environment if the driver container image is mirrored to the private registry. Disabling driver management would require manual driver installation, which is more complex and not necessary. Therefore, this is not a required action for the scenario.

Ensure the cluster nodes have direct access to the NVIDIA licensing server for vGPU.

Why it fails: This scenario does not mention vGPU; it is about GPU Operator on an air-gapped cluster. Even if vGPU were used, the licensing server would need to be accessible or a licensing proxy set up, but that is not a requirement for the GPU Operator itself. The question focuses on deploying the GPU Operator, which does not require licensing server access. Thus, this is not a required action.

Pin each experiment job to a dedicated physical GPU using node affinity

Why it fails: Dedicating a physical GPU to each short-lived job prevents sharing and leaves the GPU idle whenever a job finishes or waits on data loading. This approach lowers utilization rather than improving it, and it does not address the intermittent idle periods that occur between short experiment runs.

Disable the NVIDIA device plugin and mount GPUs directly via hostPath

Why it fails: Bypassing the device plugin removes Kubernetes' ability to track and schedule GPU resources, leading to conflicts and unpredictable placement. It does not improve utilization; instead it undermines scheduling accuracy and can cause multiple pods to unknowingly contend for the same device, which is worse than the current situation.

Increase the pod's nvidia.com/gpu request to reserve more GPU memory

Why it fails: Requesting more GPUs reserves additional devices and reduces the number of jobs that can run concurrently. For short experiments that do not need multiple GPUs, this wastes capacity and increases idle time, moving utilization in the wrong direction and contradicting the stated objective.

NVIDIA GPU Operator for Kubernetes

Why it fails: The GPU Operator is designed for Kubernetes clusters, not for vSphere hypervisors. It automates driver and container toolkit deployment on Kubernetes nodes. It does not enable vGPU on ESXi; that requires the vGPU Manager VIB. Using the GPU Operator here would not provide GPU virtualization to VMs on ESXi.

NVIDIA CUDA Toolkit installed on the ESXi host

Why it fails: The CUDA Toolkit is a development environment for GPU-accelerated applications. It is installed in guest VMs or containers, not on the ESXi hypervisor. ESXi does not run user applications, so installing CUDA Toolkit there would not enable vGPU. The host only needs the vGPU Manager.

NVIDIA Container Toolkit installed on the ESXi host

Why it fails: The NVIDIA Container Toolkit is used to enable GPU access for containers, typically on Linux hosts running Docker or containerd. ESXi is a hypervisor and does not run containers natively. Installing the toolkit on ESXi is neither supported nor useful for vGPU. It is irrelevant to vGPU deployment on vSphere.

Reset the GPU thermal throttling threshold.

Why it fails: Thermal throttling is a hardware safety mechanism that should not be adjusted to fix network latency issues. Lowering thresholds risks hardware damage, and increasing them does not address the communication delays inherent in the network fabric. Focus on fabric diagnostics rather than modifying critical hardware safety parameters.

Update the OS kernel to the latest version.

Why it fails: Blindly updating the kernel can introduce stability regressions in driver modules like OFED or NVIDIA GPU drivers. Troubleshooting should follow a structured approach starting with diagnostics rather than system-wide updates. Unnecessary kernel changes often complicate the environment, making it harder to identify the root cause of the latency.

Increase the system swap space to 512GB.

Why it fails: Increasing swap space is not a solution for InfiniBand network latency. Training jobs should avoid swapping entirely to maintain performance. Relying on swap indicates a lack of system memory, which requires addressing the workload footprint or adding physical RAM, not expanding disk-based virtual memory which is too slow.

Manually install NVIDIA drivers on every host OS.

Why it fails: Manual installation is discouraged in containerized environments like OpenShift because it bypasses the automated lifecycle management provided by the GPU Operator. Manual drivers are difficult to patch, prone to version mismatches with the container runtime, and do not persist across node reboots or OS updates automatically.

Install the NVIDIA Triton Inference Server.

Why it fails: The inference server has no role in the low-level installation of system drivers or kernel-mode components. It is an application-layer service that relies on the drivers already being present, so attempting to use it for driver configuration would be ineffective and logically incorrect for infrastructure setup.

Set the Kubernetes scheduler to 'AlwaysPull'.

Why it fails: The scheduler's pull policy dictates how images are fetched from registries but does not influence driver loading or kernel module operations. Driver configuration is handled by the GPU Operator's daemonsets and host-path mounts, making image pull policies irrelevant to the successful installation of NVIDIA hardware software components.

Disable ECC memory on all GPUs to increase raw clock speed.

Why it fails: Disabling ECC memory provides a small amount of additional memory but does not increase clock speed. It introduces significant risks of data corruption in large-scale AI models, which is unacceptable for production environments where reliability and data integrity are prioritized over negligible performance gains.

Switch the GPU power mode to maximum performance via NVML.

Why it fails: While this ensures the GPU maintains high clocks, it does not address the fundamental issue of concurrency. In inference, the goal is to pack more work into the same time window, which is achieved through resource sharing, not merely by increasing the power profile of the GPU.

Increase the batch size to the maximum allowed by GPU memory.

Why it fails: Increasing batch size indefinitely leads to increased latency, which is detrimental for real-time inference applications. While it may increase throughput, it violates the latency requirements standard in most inference pipelines, making it a poor optimization strategy for high-concurrency environments that require rapid response times.

Each node must have at least one Multi-Instance GPU profile pre-created.

Why it fails: MIG profiles are configured after the driver is loaded and are optional, used when partitioning a GPU for multiple tenants. They have no bearing on whether the driver container can compile and insert the kernel module, so this is not a prerequisite for a driver-managed installation.

The kubelet must be configured with the NVIDIA device plugin endpoint flag.

Why it fails: The device plugin is deployed by the GPU Operator and registers with kubelet through the standard plugin socket directory; administrators do not pre-configure a special kubelet flag. Setting an unsupported endpoint flag is unnecessary and can even interfere with normal device plugin registration during installation.

The cluster must have the NVIDIA Network Operator installed first.

Why it fails: The Network Operator manages RDMA and high-speed fabric components such as Mellanox NICs; it is unrelated to building and loading the GPU kernel module. A cluster can run the GPU Operator successfully with no Network Operator present, and requiring it first would block otherwise valid single-node or compute-only deployments.

NVIDIA Persistence Daemon

Why it fails: The NVIDIA Persistence Daemon is not deployed by the GPU Operator. It is a host-level service that maintains the NVIDIA driver state and is typically used in non-Kubernetes environments. It does not enable GPU scheduling or container access in Kubernetes. Therefore, it is not a component deployed by the GPU Operator for this purpose.

NVIDIA MIG Manager

Why it fails: The NVIDIA MIG Manager is an optional component of the GPU Operator used to configure Multi-Instance GPU (MIG) on supported GPUs. It is not required for basic GPU scheduling and container access. While it can be deployed, it is not one of the two essential components that enable GPU scheduling and container GPU access in a standard deployment.

NVIDIA GPU Driver

Why it fails: The NVIDIA GPU Driver is installed by the GPU Operator, but it is not a component that enables scheduling or container access directly. It provides the low-level driver, but the device plugin and container toolkit are needed to integrate with Kubernetes and containers. The driver is a prerequisite, but the question asks for components that enable scheduling and container access.

A PriorityClass that assigns a low priority value to all training pods in the namespace.

Why it fails: PriorityClass influences scheduling order and preemption, not the aggregate quantity of GPUs a namespace can hold. A low priority makes pods more likely to be evicted but does not enforce a hard ceiling, so it fails to guarantee that each namespace stays within a defined GPU budget.

A PodSecurityPolicy that denies privileged containers in the namespace.

Why it fails: PodSecurityPolicy, now removed in recent Kubernetes versions, controlled security contexts such as privileged mode and volume types. It has no concept of GPU quantity and cannot cap or reserve accelerators, so it does nothing to stop a namespace from exhausting the cluster's GPU pool.

A NetworkPolicy that restricts traffic between pods in different namespaces.

Why it fails: NetworkPolicy governs Layer 3 and Layer 4 connectivity between pods, not resource consumption. It cannot limit how many GPUs a namespace requests or prevent GPU monopolization, so it is irrelevant to enforcing per-namespace GPU capacity limits in this scenario.

Add the node's hostname and IP address to the /etc/hosts file on all cluster nodes.

Why it fails: While hostname resolution is important for cluster communication, Base Command Manager uses its own database and configuration management to track nodes. Manually editing /etc/hosts on all nodes is not a required step for integration; the manager handles name resolution through its internal mechanisms or DNS. This action alone does not register the node or make it available for scheduling.

Configure a static IP address for the node and add it to the cluster's DNS server.

Why it fails: While static IP addresses and DNS are common in cluster environments, Base Command Manager does not require manual DNS configuration for node integration. The manager can operate with its own hostname resolution and typically manages network settings during provisioning. This step is not mandatory and does not directly contribute to the node being recognized by the manager or scheduler.

Manually install the NVIDIA GPU driver on the node without using Base Command Manager's provisioning tools.

Why it fails: Base Command Manager typically manages software provisioning, including GPU drivers, through its image management and provisioning tools. Manually installing drivers bypasses the manager's control and can lead to inconsistencies. The correct approach is to use the manager's provisioning to ensure the node has the correct driver version and configuration, which is part of the integration process, not a separate manual step.

Immediately reboot the node and re-run the training job to see whether it fails again.

Why it fails: A reboot clears volatile GPU state, including row-remapper records, Xid history in some cases, and ECC counters held only in the driver. Re-running the job before collecting diagnostics destroys the evidence that would show whether the GPU had a pending remap or escalating errors. This makes the hardware-versus-software determination harder and can hide an intermittent fault that only appears under load.

Reset the GPU with `nvidia-smi --gpu-reset` and continue using the node if training succeeds afterward.

Why it fails: A GPU reset wipes the very state investigators need, and after an Xid 79 the device often cannot be reset successfully without a full node reboot anyway. Continuing to schedule production work on a GPU with a suspected row-remap or PCIe link fault risks silent data corruption and repeat job failures. Evidence collection must occur before any reset attempt.

Delete and recreate the NGC container, then pull the image again from the registry.

Why it fails: A container image contains userspace libraries, not the kernel driver or GPU firmware that produce Xid 79. Re-pulling the image changes nothing about the GPU falling off the PCIe bus, and it discards the chance to gather logs from the failing environment. This action treats a hardware or driver-level fault as an application packaging problem, which misdirects the investigation.

Add a node selector that allows multiple pods to bind to the same nvidia.com/gpu resource slot.

Why it fails: A node selector only filters which nodes a pod may run on; it does not change how GPU resources are counted or allocated. The scheduler still treats each nvidia.com/gpu unit as consumable by exactly one pod unless a sharing mode is configured. Node selectors therefore cannot enable multiple pods to occupy the same physical GPU slot.

Create a ResourceQuota that counts GPU requests as fractional values such as 0.5 per pod.

Why it fails: Kubernetes does not support fractional extended resources like nvidia.com/gpu; requests must be whole integers unless a sharing mechanism changes how devices are advertised. A ResourceQuota merely enforces limits on those integer requests. Defining fractional values in quota will not let two pods share one GPU and will likely be rejected or ignored.

Raise the GPU Operator's driver version so the device plugin reports extra virtual GPUs per card.

Why it fails: Driver versions do not create additional schedulable GPU resources. The number of advertised devices is governed by the device plugin's configuration, such as MIG or time-slicing settings, not by the driver release. Upgrading the driver might fix compatibility issues, but it will not by itself allow more pods to share a single physical GPU.

Install the NVIDIA Network Operator and enable RDMA device advertisement on the node

Why it fails: The Network Operator manages high-speed networking components such as Mellanox NICs and RDMA for distributed training, not GPU partitioning. Enabling RDMA advertisement has no effect on MIG instance creation or on how the device plugin names GPU slice resources, so it cannot make MIG-backed resources schedulable to inference pods.

Create a RuntimeClass that points to the nvidia-container-runtime and reference it from each inference pod

Why it fails: A RuntimeClass selecting nvidia-container-runtime is needed for GPU access generally, but it does not create or advertise MIG instances. It is orthogonal to MIG geometry and resource naming. Configuring it alone leaves the node without MIG resources, so pods still cannot schedule against a MIG profile even though the runtime is correctly selected.

Apply a ResourceQuota that limits nvidia.com/gpu to zero in the tenant namespace

Why it fails: Setting the GPU quota to zero would actively prevent any GPU or MIG resource consumption in that namespace, which is the opposite of the goal. ResourceQuota governs aggregate consumption and does not create MIG instances or advertise profile-specific resource names, so it cannot enable scheduling of MIG-backed pods.

Increase the NCCL timeout to allow the job to recover from the ECC error.

Why it fails: NCCL timeout settings control communication retries, not hardware ECC errors. Uncorrectable ECC errors are hardware-level faults that cannot be resolved by extending timeouts. Adjusting NCCL parameters would not prevent the job from failing due to the GPU error. The focus should be on diagnosing and isolating the faulty hardware, not on NCCL configuration.

Restart the training job immediately to see if the error recurs.

Why it fails: Restarting the job without diagnosing the ECC error may lead to repeated failures and wasted compute time. Uncorrectable ECC errors often indicate hardware degradation, and simply restarting does not address the root cause. It is better to first gather diagnostic information and then decide on a mitigation strategy, such as draining the GPU or replacing it.

Use nvidia-smi --gpu-reset to reset the GPU and clear the error state.

Why it fails: nvidia-smi --gpu-reset can clear transient errors, but it is not a diagnostic step and may not resolve persistent uncorrectable ECC errors. It also disrupts any running jobs on that GPU. Before resetting, it is important to check ECC counts and logs to understand the nature of the error. Resetting should be a last resort after diagnosis.