Multi-Region training with Amazon SageMaker HyperPod and Qumulo
Amazon Web Services Bryan Berezdivin ● Covered by 2 sources
AWS says HyperPod can train in one Region while data stays in another. Qumulo’s cache makes the remote setup hit near-local speed after a short warmup.
Based on reporting by Amazon Web Services, Bryan Berezdivin — read the original for the full story.
Summary, retelling and take written by AI under human oversight; images are AI-generated illustrations. How we work · Report an error
AWS is pitching a neat workaround for a very old headache: your GPUs are in one place, your data is in another, and neither wants to wait. With Amazon SageMaker HyperPod and Qumulo, training jobs can read from a dataset kept in another AWS Region, or even on premises, without copying the files or changing the code.
The company validated that claim with the same training run on two clusters. One sat in US East (Ohio), right next to the data. The other ran in US West (Oregon) and pulled the data remotely with 60 ms of latency. After a short warmup, the Oregon cluster caught up and matched the Ohio cluster’s throughput.
The test setup was not toy-sized. AWS used a 1.02 billion-parameter LLaMA v3 run on two ml.p5.48xlarge instances per HyperPod cluster, for 16 H100 GPUs total. The hub cluster delivered 116–117 samples per second, while the warm-cache spoke cluster reached 115–116 samples per second. In the cold-start case, the spoke began at 95–115 samples per second and then converged to hub-level speed, with the initial 100–150 batches taking 15–20 percent longer.
The pitch rests on Qumulo’s Cloud Data Fabric and NeuralCache. Qumulo keeps a single authoritative copy of the dataset on a hub cluster, then projects it to spoke clusters over VPC peering. NeuralCache watches the data loader’s 4 KB read pattern, prefetches what it expects next, and serves warmed data from local NVMe. AWS says that once the cache is warm, 94–96 percent of reads come from local storage at sub-5 ms latency.
There’s also a practical angle here that will matter to anyone who has watched petabytes slosh around just to satisfy a training job. AWS says the setup avoids full data replication, keeps the same mount path in both Regions, and still lets HyperPod handle the usual managed-training chores like health checks, node replacement, and checkpoint recovery. For teams that want cross-Region training without turning storage into a logistics project, that’s the real sales pitch.
My take — AI-written commentary, not fact-checked reporting
This is the kind of cloud story that actually has teeth: move compute where the GPUs are, leave the data where it already is, and let the cache do the boring work. It’s also a quiet rebuke to the industry’s favorite habit of treating replication as a personality trait. The real winner here is anyone tired of paying twice, once in transfer costs and again in patience.
Read more about this at: Amazon Web Services