Explanation:Measuring power consumption with a power meter while running a synthetic benchmark provides the most accurate assessment. 'nvidia-smi' reports GPU power consumption, but it doesn't account for the power draw of other components (CPU, memory, etc.). Idle power measurements are irrelevant for assessing peak load. BIOS readings can be unreliable. Simply adding up component power ratings doesn't account for inefficiencies or dynamic power management.

The recommended approach is to use a base image that already contains the desired CUDA version. NVIDIA provides pre-built images on NGC (NVIDIA GPU Cloud) that are specifically designed for deep learning and include the appropriate CUDA versions and other dependencies. Installing CUDA on the host and expecting it to be magically mapped (A) is not reliable. The NVIDIA Container Toolkit doesn't install CUDA on the fly (B). Manually copying libraries (D) is error-prone and doesn't handle dependencies well. While technically possible, using nvidia- container-cli to modify the image is more complex than using a base image.

DSCP marking, traffic class mapping, and WFQ/Strict Priority Queueing are fundamental QOS mechanisms. DSCP marking allows you to classify traffic based on application requirements. Traffic classes within the switch provide different levels of service. WFQ and Strict Priority Queueing ensure that high-priority traffic receives preferential treatment on egress ports. The other options are less relevant to QOS guarantees for a specific application.

A GPU stuck in the 'P8' power state indicates that it's not drawing the power it needs to operate at full performance. Insufficient power delivery is the most likely cause. While the new GPU could potentially be overheating or requiring a firmware update, checking power connections and PSU capacity is the first step. Comparing the new GPU's model with the others is also useful, but 'P8' state strongly suggests a power issue. Driver issues are less likely to cause a specific 'P8' state; they typically result in more general performance problems.

GPUDirect RDMA enables direct memory access between GPUs, bypassing the CPU and reducing latency. This significantly improves performance for applications that require frequent data transfers between GPUs. Other options describe functionalities that are not associated with RDMA in this context.