Hard Difficulty Questions

The system has reached the max_concurrent_jobs limit.

Why it fails: While the policy limits concurrent jobs to two, the stem specifically mentions hitting 'resource limits' related to memory. The memory constraint is an independent limit defined in the policy. The error reported is clearly tied to the resource quota configuration, not the job count limit defined in the JSON.

The NVIDIA driver is blocking the user's access.

Why it fails: The NVIDIA driver does not enforce per-user memory quotas; this is handled by a higher-level orchestration or scheduling layer. The JSON policy indicates an management-level configuration file, not a driver-level limitation. The error is due to the policy quota, not a restriction imposed by the underlying low-level hardware driver.

The GPU memory is fragmented across the nodes.

Why it fails: Fragmentation can cause issues, but in this context, the specific JSON quota is the bottleneck being applied. The administrator is looking at a policy-driven limit. Fragmentation is a low-level memory management issue that would manifest differently than a hard limit defined by an administrative resource policy configuration file.

The container gains elevated privileges to access the host GPU

Why it fails: Access to device files does not confer elevated privileges; it simply provides the required interface for the driver library to communicate with the hardware. Restricting these files prevents the container from seeing or using the GPU at all, which is the opposite of providing elevated access or permissions.

The container can use the GPU but cannot perform memory mapping

Why it fails: GPU communication is binary; the driver either successfully initializes the device or it does not. There is no partial state where a container can use the GPU for logic but fail at memory mapping. If the device nodes are blocked, the entire interface is rendered completely unusable.

The container experiences increased latency for GPU operations

Why it fails: Latency is a measure of performance degradation, not a binary access control outcome. If the policy denies access to the hardware devices, the application will not experience latency; it will encounter a catastrophic failure or segmentation fault immediately upon attempting to query the GPU device drivers.

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.

The GPU driver is corrupted and requires a reinstall.

Why it fails: Corrupted drivers usually result in 'device not found' or kernel initialization failures rather than an 'invalid ordinal' error. An invalid ordinal refers to a specific integer index that is out of range, pointing to a configuration issue regarding how the application is selecting the hardware, not a driver malfunction.

The GPU memory is full.

Why it fails: Memory fullness results in OOM (Out of Memory) errors, not an 'invalid device ordinal' error. The system knows the device exists, but the allocation request fails. An invalid ordinal implies that the device selection itself is the point of failure, regardless of the memory state of any physical GPU.

The InfiniBand fabric is misconfigured.

Why it fails: InfiniBand issues affect communication latency and connectivity between nodes, not the local indexing of GPUs within the host system. CUDA device indexing is handled by the local driver and runtime, independent of the network fabric. Misconfigurations here would manifest as NCCL errors, not device ordinal errors at startup.

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.

The CUDA driver version is incompatible with the installed GPU.

Why it fails: Driver incompatibility typically results in runtime initialization failures or errors when calling CUDA APIs, rather than OOM errors during workload execution. OOM errors specifically relate to resource exhaustion, and while driver issues might exacerbate performance, they are not the primary cause of an OOM error during normal operations.

The GPU device plugin is not correctly reporting free memory.

Why it fails: While possible, the device plugin failure would typically prevent the pod from scheduling or result in allocation errors at startup. If the workload is actually executing and then failing with an OOM, it is far more likely to be an application-level memory issue rather than a plugin reporting error.

The training job is using mixed-precision training (FP16).

Why it fails: Mixed-precision training is designed to reduce the memory footprint of a model, actually mitigating the risk of OOM errors rather than causing them. It is a standard technique used to fit larger models into limited GPU memory and would not be the cause of an OOM crash.

The PriorityClass value is too low to trigger preemption.

Why it fails: A value of one million is significantly higher than the standard Kubernetes system defaults, which typically range from 1,000 to 10,000. Increasing the value further would not resolve the issue if the underlying issue is related to Gang Scheduling constraints or resource placement logic, rather than actual preemption.

The NVIDIA Device Plugin requires a restart to recognize the PriorityClass.

Why it fails: The NVIDIA Device Plugin does not require a restart to acknowledge new PriorityClass objects. The scheduling logic is handled by the control plane and the scheduler component; the plugin simply advertises the GPU resources available to the scheduler, which makes decisions based on existing cluster state information.

The PriorityClass name violates Kubernetes naming conventions.

Why it fails: The name 'high-priority-gpu' is a valid alphanumeric string following standard Kubernetes resource naming conventions. The issue is logically rooted in how the scheduler interprets resource requirements versus cluster availability, not a syntax error within the manifest file itself, which would have been rejected during initial creation.

The total number of CPU threads used.

Why it fails: While CPU performance is important for data loading, the total number of CPU threads has a negligible impact on the actual GPU memory required to store the model, activations, and optimizer states. It is a secondary performance factor, not a primary driver of GPU memory demand for LLMs.

The network latency between nodes.

Why it fails: Network latency is critical for communication speed in distributed training (e.g., NCCL throughput), but it does not dictate how much memory is allocated on the GPU itself. While poor latency affects overall job time, it is not a direct factor in calculating the required GPU memory for model loading.

Increase the GPU clock speed via nvidia-smi.

Why it fails: Increasing the GPU clock speed will increase power consumption and heat, and it does not resolve the bottleneck of data transfer between host and device. If the system is already bound by I/O and data movement, changing the clock speed provides negligible performance gains for the application.

Switch to FP64 precision for higher accuracy.

Why it fails: Switching to FP64 precision significantly increases the amount of data that needs to be transferred and processed, making the I/O bottleneck worse. It also consumes more memory and compute resources, which would likely lead to a drop in total throughput rather than the improvement the administrator needs.

Implement multi-threaded data preprocessing on the GPU.

Why it fails: Moving preprocessing to the GPU does not solve the initial host-to-device transfer bottleneck. In fact, if the data is already on the host, sending it to the GPU is still required. The bottleneck is the transfer efficiency, which is addressed by pinned memory, not the preprocessing location.

Through the BIOS settings, creating physical partitions on the GPU die.

Why it fails: MIG is a hardware-level feature managed by the driver, not a physical partition created in the BIOS. BIOS settings are not capable of dividing GPU compute units; attempting this would be impossible and could result in unstable hardware configurations that prevent the OS from booting correctly.

By editing the /etc/nvidia/mig-config.json file directly.

Why it fails: There is no standard configuration file for MIG. MIG instances must be created and modified using the official NVIDIA-provided management utilities. Editing arbitrary files is not a supported method and will not be recognized by the driver, leading to a failure to partition the GPU as expected.

Via the OS kernel boot parameters in the GRUB configuration.

Why it fails: Kernel boot parameters are used for low-level system configuration, not for managing GPU-specific partitioning features like MIG. While some drivers might be loaded via kernel parameters, MIG instance management is a dynamic driver-level operation that must be handled while the system is actively running.

The GPUs have different amounts of physical memory.

Why it fails: If the GPUs had different physical memory, the system would likely throw an error or fail to initialize the distributed environment. The memory usage is similar, which suggests the hardware is physically capable, but the software is failing to schedule compute tasks across both devices effectively.

The system is limited by the PCIe bus bandwidth.

Why it fails: PCIe bandwidth issues would cause both GPUs to show low utilization while waiting for data. In this scenario, one GPU is performing at near-maximum capacity, which proves the bus can handle the data load; the bottleneck is specifically the application logic distributing tasks to only one device.

The model is too small to be parallelized across two GPUs.

Why it fails: Even small models can be parallelized using techniques like batch parallelism. The issue here is not the model size, but rather the execution environment. The presence of high memory usage on both GPUs suggests the model is loaded on both, but the compute is not being distributed.

vGPU with MIG partitioning

Why it fails: MIG partitioning creates isolated GPU instances, but it does not support vMotion. MIG instances are tied to the physical GPU and cannot be migrated across hosts. While MIG provides isolation, it is not compatible with vMotion, which requires the GPU to be virtualized in a way that allows migration. Thus, this mode is incorrect for the scenario.

vGPU with time-sliced scheduling

Why it fails: Time-sliced vGPU allows multiple VMs to share a physical GPU by time-slicing, but it does not support vMotion because the GPU state cannot be migrated. vMotion requires the GPU to be in a state that can be suspended and resumed on another host. Time-sliced vGPU does not provide that capability, so it fails the requirement.

vGPU with SR-IOV

Why it fails: SR-IOV provides direct hardware access to VMs, but it does not support vMotion. SR-IOV virtual functions are tied to the physical function and cannot be migrated. vMotion requires the GPU to be virtualized with a migration-capable mode. SR-IOV is not that mode, so it is not suitable for this requirement.

Set "compute_mode" to "exclusive_process".

Why it fails: Changing the compute mode to exclusive_process prevents multiple processes from sharing the GPU, but it does not address the thermal output of the hardware. The GPU will still generate the same amount of heat, meaning thermal throttling will continue to occur regardless of the process mode.

Disable ECC mode to lower the power consumption.

Why it fails: Disabling ECC mode can lead to data corruption in large-scale training jobs, which is unacceptable for model reliability. Furthermore, the power saving from disabling ECC is negligible compared to the total thermal output of the GPU, making this an ineffective strategy for preventing thermal throttling.

Set "persistence" to "disabled".

Why it fails: Disabling persistence mode would result in the driver re-initializing the GPU between tasks, adding overhead and increasing latency. It does not reduce the heat generated by the GPU under load and will not prevent thermal throttling during active training operations.

Apply a ResourceQuota limiting nvidia.com/gpu to one per namespace

Why it fails: A ResourceQuota restricts aggregate namespace consumption but does not isolate memory between pods already scheduled onto the same physical GPU. Two pods in different namespaces could still share a time-sliced GPU and contend for memory. The quota also would not prevent the scheduler from placing the training pod alongside an inference pod, so the interference remains.

Set the CUDA_VISIBLE_DEVICES environment variable manually in the training pod spec

Why it fails: Manually setting CUDA_VISIBLE_DEVICES bypasses the device plugin's allocation logic and can conflict with the kubelet's injected value. It also does not create memory isolation; another pod on the same time-sliced GPU can still consume memory. This approach risks scheduling errors and does not address the root cause of cross-pod GPU memory contention.

Increase the pod's nvidia.com/gpu request to two GPUs

Why it fails: Requesting a second GPU does not prevent another pod from sharing the first GPU when time-slicing is enabled, because the device plugin still advertises replicas of each physical device. The training pod may receive two virtual GPU replicas that map to shared physical devices, so the concurrent memory consumption problem persists rather than being resolved.

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.

Configure the device plugin with `--fail-on-init-error=false` and redeploy the services with lower memory limits.

Why it fails: This flag changes plugin startup behavior when the driver or device initialization fails; it does not create additional allocatable GPU slots or partition the device. Lower memory limits constrain the container's host memory, not GPU memory, so the second and later replicas would still remain Pending for lack of an allocatable nvidia.com/gpu resource.

Set `nvidia.com/gpu: 0.5` on each pod's resource requests so the device plugin can subdivide the GPU among replicas.

Why it fails: Kubernetes extended resources must be requested as integers; a fractional request like 0.5 is rejected by the API server and the pod never schedules. The device plugin cannot subdivide a device through fractional quantities, so this approach does not produce shareable GPU slots and the services would fail admission validation.

Enable time-slicing in the device plugin's ConfigMap so pods share the GPU, and set `replicas` equal to the number of services.

Why it fails: Time-slicing does let multiple pods share one GPU, but it advertises the same device multiple times without memory isolation, and it requires editing the device plugin configuration via the GPU Operator's ConfigMap rather than a simple replicas change. It is a valid sharing mode but the weaker fit here because memory-partitioned isolation is available on A100 and the services are distinct tenants.

Create a ResourceQuota per tenant that caps GPU usage, and rely on the quota controller to reclaim GPUs from Tenant B when Tenant A's jobs arrive.

Why it fails: ResourceQuota limits how much a namespace may consume but does not evict running pods to free capacity for another tenant. Once Tenant B's pods are bound and consuming GPUs, the quota controller does not reclaim them for Tenant A. This leaves the training jobs waiting while inference pods keep their devices, so the requirement to reclaim capacity is unmet.

Assign Tenant A's training pods a high-priority PriorityClass with preemptionPolicy: PreemptLowerPriority, and give Tenant B's inference pods a lower-priority class.

Why it fails: Setting the training pods to preempt lower-priority work would indeed let them reclaim capacity, but assigning Tenant B's protected inference pods a lower class makes them eligible for eviction. That directly violates the requirement that inference pods remain untouched. The scenario needs a way to shield specific pods from preemption, which a simple priority ordering across tenants cannot provide.

Enable time-slicing on all GPUs so Tenant A's jobs share devices with Tenant B's inference pods, avoiding preemption entirely.

Why it fails: Time-slicing lets multiple pods share a physical GPU, but it offers no priority semantics and no guarantee of reclaiming capacity. Tenant A's training jobs would contend with inference workloads on the same engine, potentially degrading latency, and could still be starved if slots are occupied. Sharing devices does not satisfy the requirement to reclaim capacity while protecting the inference pods.

Enable time-slicing with a replica count of four so the notebook and batch workloads alternate on the same GPU in round-robin fashion.

Why it fails: Time-slicing interleaves execution but does not partition memory, so all containers still share the full framebuffer. If the batch workload allocates heavily, it can still exhaust memory and cause the notebook to fail, which means this approach does not deliver the isolation the team needs.

Set a memory limit on the batch container using the standard Kubernetes resources.limits.memory field to cap its GPU framebuffer usage.

Why it fails: The resources.limits.memory field governs host system memory, not GPU device memory. Applying it would restrict the container's RAM and could trigger host-level OOM kills, but it has no effect on how much framebuffer the CUDA workload allocates, so the notebook would remain vulnerable.

Add a higher PriorityClass to the notebook pod so the kube-scheduler preempts the batch workload whenever memory pressure occurs.

Why it fails: PriorityClass affects scheduling and preemption decisions at admission time; it does not react to runtime GPU memory pressure or evict a running workload because of an allocation failure. The batch pod would keep its framebuffer, and the notebook would still crash, so priority cannot substitute for memory isolation.

Disable the NVIDIA Container Toolkit to bypass runtime restrictions.

Why it fails: Disabling the NVIDIA Container Toolkit would prevent any GPU-accelerated application from running. It is required for both standard and confidential computing deployments to function. The toolkit provides the necessary runtime hooks that facilitate the communication between the container and the hardware, even within a secure, confidential environment.

Set the GPU persistence mode to 'off' to ensure security.

Why it fails: Persistence mode keeps the driver loaded even when no applications are using the GPU. Disabling it has no bearing on confidential computing security. In fact, persistence mode is generally recommended in production for faster job startup times and to prevent unnecessary driver re-initialization cycles between short-lived AI training jobs.

Set the NCCL_DEBUG environment variable to INFO to force InfiniBand usage.

Why it fails: NCCL_DEBUG only controls logging verbosity; it does not influence the transport selection. Setting it to INFO will provide more logs but will not enable InfiniBand if it is disabled. It is a diagnostic tool, not a configuration for transport.

Install and configure the NVIDIA Fabric Manager on all nodes.

Why it fails: Fabric Manager is required for NVLink/NVSwitch management within a node, not for InfiniBand communication between nodes. While it is important for intra-node GPU communication, it does not affect NCCL's choice to use InfiniBand for inter-node communication.

Set the NCCL_SOCKET_IFNAME environment variable to the Ethernet interface.

Why it fails: NCCL_SOCKET_IFNAME specifies the network interface for socket communication, typically used for bootstrap or when InfiniBand is not available. Setting it to Ethernet would force NCCL to use Ethernet for communication, which is the opposite of the desired outcome.