While the other options could contribute to performance issues, the subnet manager (SM) is the MOST likely culprit. A poorly configured or malfunctioning SM can lead to suboptimal path selection (e.g., routing traffic through congested links or longer paths), which significantly increases latency and reduces bandwidth. Congestion control mechanisms, if not properly configured, can also fail to mitigate congestion, leading to packet loss and retransmissions, further degrading performance. Checking the SM logs and configuration is the first step in diagnosing this issue. Incorrect NIJMA placement, small MTIJ, powersave governor, and memory registration limits could also impact performance but are less likely to be the primary bottleneck if the links are up.

You can configure 'pip' to use a proxy by setting environment variables (A), using the '-proxy' option with the 'pip install' command (B), or creating a 'pip.conf file (C). Downloading the package manually (D) is a workaround for the initial installation but doesn't address ongoing communication with NGC. Option E is incorrect since NGC CLI does support proxy configuration.

The most robust solution for distributed GPU utilization is to leverage a container orchestration platform like Kubernetes (B) along with the NVIDIA Container Toolkit. Kubernetes handles scheduling, resource allocation (including GPUs), and networking across multiple nodes.
The NVIDIA Container Toolkit ensures that each container can access the GPUs on its host. While (C) is useful, it's not sufficient for multi-server deployments. Docker Swarm (D) can work but lacks the sophisticated GPU scheduling capabilities of Kubernetes. NFS sharing (A) is unnecessary and can introduce performance bottlenecks. A custom Docker network (E) doesn't directly address GPU access.

BeeGFS and Lustre are designed for high-performance computing and AI workloads, offering high bandwidth, low latency, and POSIX compliance. GlusterFS and Ceph are more general-purpose distributed file systems. NFS is generally not suitable for demanding AI workloads due to its performance limitations.

Each GPU requires 4GB/s of bandwidth. The network has a bandwidth of 25GB/s. Therefore, the maximum number of GPUs that can be saturated is 25GB/s / 4GB/s per GPU = 6.25 GPUs. Since you can't have a fraction of a GPU, the answer is 6 GPUs.