NEWS · MODELS · #1045
Amazon SageMaker HyperPod and Qumulo enable multi‑Region model training without copying data
Amazon and Qumulo validated an architecture pairing Amazon SageMaker HyperPod with Cloud Native Qumulo (CNQ) and Qumulo Cloud Data Fabric (CDF) so HyperPod clusters can train from a single dataset stored in a different AWS Region without replicating data. In a cross‑Region test (hub in us-east-2, spoke in us-west-2) running a 1.02B‑parameter LLaMA v3 job on two ml.p5.48xlarge instances per cluster (16 H100 GPUs total), the remote spoke converged to hub throughput (≈115–117 samples/sec) after a short NeuralCache warmup, achieving near‑full GPU utilization thereafter.
KEY POINTS
- Amazon and Qumulo validated an architecture pairing Amazon SageMaker HyperPod with Cloud Native Qumulo (CNQ) and Qumulo Cloud Data Fabric (CDF) so HyperPod clusters can train from a single dataset stored in a different AWS Region without replicating data.
- In a cross‑Region test (hub in us-east-2, spoke in us-west-2) running a 1.02B‑parameter LLaMA v3 job on two ml.p5.48xlarge instances per cluster (16 H100 GPUs total), the remote spoke converged to hub throughput (≈115–117 samples/sec) after a short NeuralCache warmup, achieving near‑full GPU utilization thereafter.
- This reduces the need to replicate petabyte‑scale training data across Regions and lets teams run frontier training where the compute is available while preserving throughput and utilization.
WHY IT MATTERS
This reduces the need to replicate petabyte‑scale training data across Regions and lets teams run frontier training where the compute is available while preserving throughput and utilization.