Hot-swapping is the designed method for replacing redundant PSUs in many server systems. This allows the server to remain operational while the faulty PSU is replaced, minimizing downtime. Powering down the server immediately is unnecessary and causes downtime. Waiting for a maintenance window or until the remaining PSU fails increases the risk of a complete server outage.

Multiple factors can contribute to low throughput in inference serving. Model loading overhead is significant, and dynamic batching is crucial to maximize throughput. Insufficient CPU cores and memory constraints on the GPU also limit performance. Model precision reduction helps reduce memory footprint and increase throughput. While PCle bandwidth is a factor, it is often not the primary bottleneck in inference serving.

The MOST important steps involve visual inspection for damage, cleaning the connector to ensure a good optical connection, and verifying DOM information to assess the transceivers health and signal levels. OTDR is more suited for cable diagnosis. While firmware and voltage are relevant, the DOM provides immediate health indicators.

The 'mlxconfig' utility (or its equivalent for newer BlueField generations) is specifically designed for configuring Mellanox/NVlDlA network adapters, including BlueField DPUs. It provides access to a wide range of hardware settings, including PCle link speed and width. NVML is primarily for GPU management, not DPIJ configuration. 'ethtoor manages network interface settings, but not PCle link parameters. Accessing the PCle configuration space directly is possible but complex and risky. Modifying the DTB is a more permanent and low-level approach, typically used during initial board configuration, not for runtime adjustments.

Creating a log. 120gb instance for the memory-intensive LLM and a 4g.40gb instance for the inference service provides dedicated resources that cater to the specific needs of each workload, without the overhead or limitations of CUDA MPS or Kubernetes resource quotas. Option A is too conservative, potentially limiting the LLM performance. Option B sacrifices dedicated resources for inference, which may hurt latency. Option C does not leverage MIG and does not guarantee resource isolation and performance consistency. Option E introduces complexities associated with Time-Slicing and might not be suitable for real-time processing.