More replicas increase read parallelism. 'replica-auto-balance' ensures replicas are well-distributed. 'data locality minimizes network hops for reads. It is not beneficial to reduce volume size, as that is required. Disabling built-in monitoring is also a bad idea.

Quantization reduces model size. Model parallelism distributes the load. Speculative decoding and continuous batching increase throughput. And trying different compile modes can yield better performance.

NFS locking issues often manifest as errors in server/client logs. Disabling locking can be a workaround, but with risk. Ensuring NFSv4 support and using alternative storage backends address the root cause.

Capacity is obviously important for large datasets. IOPS and Network Bandwidth are critical for feeding data to the GPUs efficiently and avoiding bottlenecks during training. While CPU and RAM on storage servers are relevant, they are secondary to capacity, IOPS, and network performance. Insufficient bandwidth can lead to GPU starvation, significantly slowing down the training process.

Staging data to a high-performance parallel file system before training reduces latency by bringing the data closer to the compute nodes and providing high throughput. Directly accessing object storage introduces network latency, sharing over NFS can bottleneck, and a single SSD or HDD won't provide sufficient IOPS for multiple GPUs.