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Amazon SageMaker HyperPod offers an end-to-end experience supporting the full lifecycle of AI development—from interactive experimentation and training to inference…
This post is cowritten with Altay Sansal and Alejandro Valenciano from TGS. TGS, a geoscience data provider for the energy…
This blog post was co-authored with Johannes Maunz, Tobias Bösch Borgards, Aleksander Cisłak, and Bartłomiej Gralewicz from Hexagon. Hexagon is…
Training and deploying large AI models requires advanced distributed computing capabilities, but managing these distributed systems shouldn’t be complex for…
Foundation model training has reached an inflection point where traditional checkpoint-based recovery methods are becoming a bottleneck to efficiency and…
We are excited to announce the general availability of GPU partitioning with Amazon SageMaker HyperPod, using NVIDIA Multi-Instance GPU (MIG). With…
Amazon SageMaker HyperPod is a purpose-built infrastructure for optimizing foundation model training and inference at scale. SageMaker HyperPod removes the…
Today, we’re excited to announce that Amazon SageMaker HyperPod now supports managed node automatic scaling with Karpenter, so you can…
GPUs are a precious resource; they are both short in supply and much more costly than traditional CPUs. They are…
Climate tech startups are companies that use technology and innovation to address the climate crisis, with a primary focus on…
