HPE-curated Spark images are designed to work with HPE AI Essentials data sources; for these images, users can access configured sources without manually distributing secrets, unlike generic Spark OSS images.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie111dhen_us&docLocale=en_US& page=Spark%2Fcreating_spark_application.html

The DL384 Gen12 with NVIDIA GH200 NVL2 is purpose-built for enterprise AI and large-model workloads, including LLM inference and accelerated AI processing. The other options are mainly CPU-centric or less specialized for this healthcare AI use case.

NVIDIA Spectrum-X uses telemetry/control information with flow metering so senders can adjust traffic at the source before congestion causes packet loss. ECMP, PFC, and ECN alone do not describe Spectrum-X's differentiated congestion-control mechanism.
Reference: https://developer.nvidia.com/blog/optimize-large-scale-ai-workloads-with-nvidia- spectrum-x/

The DL380a Gen12 is accelerator-optimized for AI inferencing, and 70% direct liquid cooling is the best fit when the customer has facility water at the rack and strict efficiency requirements. Air cooling, rear-door cooling, or closed-loop cooling would be less efficient for this scenario.
Reference: https://www.hpe.com/emea_europe/en/compute/hpe-proliant-compute/dl380a- gen12.html

The HPE AI Essentials scheduler plug-in manages GPU/resource allocation for concurrent workloads and supports workload priority handling, which is the feature relevant to multiple users competing for shared AI resources.