Scaling seismic foundation models on AWS: Distributed training with Amazon SageMaker HyperPod and expanding context windows
This post describes how TGS achieved near-linear scaling for distributed training and expanded context windows for their Vision Transformer-based SFM using Amazon SageMaker HyperPod. This joint solution cut training time from 6 months to just 5 days while enabling analysis of seismic volumes larger than previously possible.
Why this matters
This can shift multimodal capability benchmarks and model-selection decisions.
What happened
This post describes how TGS achieved near-linear scaling for distributed training and expanded context windows for their Vision Transformer-based SFM using Amazon SageMaker HyperPod.
Who should care
Teams benchmarking model capability and cost.
Recommended next step
Benchmark this model against your baseline on latency, quality, and cost.
