AWS re:Invent 2025 - Accelerate AI workloads with UltraServers on Amazon SageMaker HyperPod (AIM362)
Source: Dev.to
Overview
Rekha Seshadrinathan (Senior Manager, Amazon SageMaker) and Paulo (Principal Specialist Solutions Architect) presented Amazon SageMaker HyperPod and EC2 Ultra servers as a unified solution for large‑scale generative AI training and inference. The session covered common AI‑workflow challenges, the capabilities of HyperPod and Ultra servers, a live demo, and advanced use‑case examples such as massive Mixture‑of‑Experts (MoE) training.
Historical Analogy
The presenters opened with the story of the Brooklyn Bridge—an engineering feat once thought impossible. They likened today’s AI breakthroughs (transformers, foundation models, RAG, reinforcement learning) to that paradigm shift, emphasizing that just as steel‑wire suspension required new architecture, generative AI demands a new compute and software stack.
Challenges in Generative AI Development
| Challenge | Impact |
|---|---|
| Compute access – high‑demand accelerated instances are often unavailable on‑demand. | Delays in starting training jobs. |
| Long‑term reservations – committing to 1–3 year reservations can lead to under‑utilization because AI workloads are spiky. | Inefficient capital expenditure. |
| Static resource allocation – administrators manually assign instances to teams, causing idle capacity and priority inversion. | Lower overall cluster utilization. |
| Model size vs. GPU memory – modern foundation models (e.g., 175 B‑parameter GPT‑3) require >350 GB of FP16 weights, far exceeding the 80 GB memory of a single H100 GPU. | Necessitates model parallelism and expertise in distributed training. |
| Hardware failures – large clusters increase the probability of node or network failures, which can interrupt long training runs. | Need for automated resiliency. |
SageMaker HyperPod
Flexible Training Plans
HyperPod lets users select training durations ranging from a single day to six months, matching budget cycles and project timelines.
Task Governance and Resource Allocation
- Priority queues and pre‑emption rules enable multi‑team sharing of a common pool of Ultra servers.
- Capacity borrowing/lending lets a team temporarily use idle resources from another team, improving overall utilization.
Pre‑benchmarked Recipes and Automated Resiliency
- HyperPod ships with validated training recipes for popular frameworks (PyTorch, TensorFlow, JAX).
- Built‑in monitoring detects node or network failures and automatically restarts affected jobs without manual intervention.
EC2 Ultra Servers
Architecture Overview
Ultra servers (GB200/GB300) integrate up to 72 GPUs per rack using a high‑speed NVLink switch and Elastic Fabric Adapter (EFA) with the Scalable Reliable Datagram (SRD) protocol.
NVLink and Elastic Fabric Adapter
- NVLink provides intra‑node GPU‑to‑GPU bandwidth exceeding 600 GB/s, reducing data movement latency.
- EFA SRD delivers low‑latency, lossless inter‑node communication, essential for large‑scale data‑parallel and model‑parallel training.
Topology‑aware Scheduling
HyperPod’s scheduler is aware of the physical GPU topology, placing related tasks on GPUs that share NVLink links and minimizing cross‑rack traffic.
Demonstration Highlights
- Creating a training plan – selected a 30‑day plan with a budget cap.
- Configuring task governance – defined two priority queues (high, low) and enabled capacity borrowing between them.
- Launching a distributed training job – the job automatically spanned 48 GPUs across two Ultra racks, with HyperPod handling checkpointing and failure recovery.
- Monitoring – real‑time dashboards showed GPU utilization, network throughput, and pre‑empted job handling.
Advanced Use Cases
Mixture of Experts at Scale
- Training a MoE model across an Ultra cluster with >25,000 interconnected GPUs.
- Achieved 60 % network optimization through topology‑aware placement and up to 68 % cost savings compared with on‑demand pricing.
Other explored scenarios included multi‑tenant inference serving, continuous fine‑tuning pipelines, and reinforcement‑learning‑from‑human‑feedback (RLHF) loops that benefit from HyperPod’s automated resiliency.
Summary
SageMaker HyperPod combined with EC2 Ultra servers provides a comprehensive platform that addresses the core challenges of generative AI workloads:
- On‑demand elasticity with flexible reservation lengths.
- Dynamic, priority‑driven resource sharing across teams.
- High‑performance interconnects (NVLink, EFA) for massive GPU clusters.
- Built‑in resiliency that abstracts hardware failures.
These capabilities enable enterprises to train and serve large foundation models efficiently, cost‑effectively, and at scale.