Infrastructure Briefing — December 4, 2024

Executive briefing: During re:Invent 2024, AWS and NVIDIA announced expanded strategic collaboration introducing Amazon EC2 P6e instances with NVIDIA Blackwell GPUs, updated DGX Cloud availability, and an enhanced EFA (Elastic Fabric Adapter) stack. Zeph Tech is advising operators on capacity reservations, interconnect benchmarks, and MLOps readiness to absorb the new accelerator tiers.

Key industry signals

Amazon EC2 P6e. The new instance family pairs NVIDIA B200 GPUs with Amazon’s fifth-generation Nitro cards, supporting 3.5 TB/s of NVLink bandwidth per node and low-latency EFA networking for training clusters.
DGX Cloud on AWS. NVIDIA confirmed DGX Cloud regions expanding across North America and Europe with managed Slurm and Base Command integrations so enterprises can burst workloads without racking on-premises hardware.
EFA performance. AWS rolled out EFA Express to deliver sub-15 microsecond latency for multi-node training jobs, enabling higher scaling efficiency on P6e and existing P5d/P5e deployments.

Control alignment

NIST SP 800-171 Rev. 3 3.4.1. Update configuration baselines documenting accelerator cluster provisioning, interconnect topology, and firmware governance for Blackwell hardware.
ISO/IEC 27001 Annex A.8.24. Capture capacity management and resilience considerations when onboarding DGX Cloud to satisfy availability and disaster recovery expectations.

Detection and response priorities

Instrument CloudWatch metrics for EFA credit exhaustion, GPU health, and fabric congestion on P6e fleets; feed anomalies to incident response playbooks.
Validate GuardDuty Malware Protection for Amazon EBS and FSx for Lustre volumes used by DGX Cloud workloads.

Enablement moves

Coordinate with procurement on reserved instance and Savings Plan options for P6e launches to secure 2025 training capacity.
Run benchmarking sprints comparing B200 performance against existing H100/H200 estates to update forecasting models and scheduling policies.

Sources

Zeph Tech’s infrastructure practice models accelerator demand, negotiates cloud commitments, and codifies runbooks so AI training clusters stay performant and compliant.