CCNA Installation and Deployment Questions

The CUDA version dictates which APIs and features are available to the AI application. Because the driver on the host must support the CUDA version used by the container (backward compatibility), mismatching these leads to runtime failures. This is a crucial AI Ops consideration as it directly affects the stability of the entire stack, ensuring that the software environment aligns with the underlying hardware capabilities for maximum performance and reliability.

The NVIDIA GPU Operator automates the lifecycle of NVIDIA software components, including the driver, toolkit, and device plugin. By configuring the operator to deploy the GPU device plugin and the Node Feature Discovery (NFD) service, the cluster gains the ability to identify GPU hardware and advertise it as an allocatable resource. Without these, the Kubernetes scheduler cannot place pods requiring GPU resources, leading to 'Pending' status for those workloads.

In multi-tenant environments, using MIG (Multi-Instance GPU) is the standard method for hardware-level isolation. MIG allows a single physical GPU to be partitioned into multiple isolated instances, each with its own dedicated compute and memory. This ensures that one tenant's workload cannot interfere with or access the resources of another, providing a robust security boundary that software-only isolation cannot reliably guarantee in accelerated computing environments.

The Persistence Daemon ensures that the NVIDIA driver remains loaded even when no applications are using the GPU. This prevents the driver from unloading and then reloading when a job starts, which significantly reduces the startup latency of AI models. It is a critical configuration for high-performance environments where frequent job scheduling would otherwise incur unnecessary overhead from repeated driver and device initialization.

The NVIDIA DCGM Exporter is the component of the GPU Operator that gathers GPU metrics via DCGM and exposes them in Prometheus format. It is specifically designed for monitoring GPU health and performance in Kubernetes. Other components like the Container Toolkit, Device Plugin, and Validator serve different purposes and do not provide metrics collection for Prometheus.

Air-gapped deployments require that all container images are available in a private registry, and the GPU Operator must be configured to pull from that registry. Mirroring the images ensures availability, and setting the image repository in the Helm values directs the Operator to the correct location. These two actions are essential for a successful deployment without internet access.

The NVIDIA Container Toolkit allows container engines, such as Docker or containerd, to interface with physical GPUs. It enables the exposure of GPUs inside containers, ensuring that applications can access CUDA libraries and hardware acceleration. Without this toolkit, the container runtime cannot bridge the gap between the host's GPU hardware and the containerized process, effectively rendering the GPU unusable for AI workloads.

The driver container builds the NVIDIA kernel module at runtime against the host kernel, so matching kernel headers or development packages must be present on each node. The GPU Operator manages the container toolkit, DCGM, and device plugin itself, so those do not need to be preinstalled. vGPU Manager applies to virtualized deployments, not bare-metal driver automation.

To enable NVIDIA vGPU on VMware ESXi, the host must have the NVIDIA vGPU Manager VIB installed, which allows the hypervisor to partition the physical GPU. Each VM that uses a vGPU must also have the NVIDIA vGPU guest driver installed. These two components are essential; other tools like the GPU Operator or Container Toolkit are not used on ESXi.

MIG allows a single GPU to be partitioned into multiple isolated instances, each with dedicated compute and memory resources. Configuring MIG via the nvidia-smi tool at the OS level ensures that these partitions are visible to the container runtime. This is crucial for AI operations, as it enables safe, secure multi-tenancy where different users can run jobs simultaneously without resource contention or cross-tenant interference.

The GPU Operator uses node selectors to determine where components are deployed. Configuring `driver.nodeSelector` with a label that identifies GPU-equipped nodes ensures driver management is scoped to those nodes only. This avoids the need for manual taints on non-GPU nodes and aligns with the operator's declarative model for heterogeneous clusters.

To maintain high-performance, synchronized training, all participating nodes should share the same GPU architecture and interconnect type (e.g., NVLink or InfiniBand). Using Kubernetes node affinity or anti-affinity rules combined with the hardware labels automatically generated by the Node Feature Discovery (NFD) service ensures that the scheduler places the job exclusively on compatible nodes. This prevents the training from falling back to slower, sub-optimal communication paths or heterogeneous hardware modes.

To use the GPU Operator with drivers already managed by the node image, the ClusterPolicy must disable driver management so the Operator skips the driver container. The container toolkit, device plugin, and DCGM exporter remain enabled so the Operator still configures runtime integration, advertises GPU resources, and exposes telemetry as the team intends.

The NVIDIA GPU Operator is essential for automating the management of all NVIDIA software components in Kubernetes. By installing it first, the administrator ensures that the device plugin, monitoring tools, and drivers are correctly configured. This foundation is critical for scheduling GPU-accelerated pods, as the Kubernetes scheduler requires the device plugin to advertise available GPU resources to the cluster's API server, enabling seamless workload orchestration across the infrastructure.

The NVIDIA Container Toolkit is essential because it allows the container runtime to interact with the host's NVIDIA drivers. It provides the necessary libraries and the container runtime wrapper to expose GPUs inside the container environment. Without this toolkit, applications inside a container cannot access the GPU hardware, even if the host has the correct drivers installed, making it a critical deployment component.