Building Blocks for Foundation Model Training and Inference on AWS

Building Blocks for Foundation Model Training and Inference on AWS Building Blocks for Foundation Model Training and Inference on AWS Enterprise Article Published May 11, 2026 Upvote 26 Keita Watanabe KeitaWatanabe amazon Pavel Belevich pbelevich amazon Aman Shanbhag amanshanbhag amazon For a long time, "scaling" in foundation models mostly meant one thing: spend more compute on pre-training and capabilities rise. That intuition was supported by empirical work such as Kaplan et al.

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• Building Blocks for Foundation Model Training and Inference on AWS Enterprise Article Published May 11, 2026 Upvote 26 Keita Watanabe KeitaWatanabe amazon Pavel Belevich pbelevich amazon Aman Shanbhag amanshanbhag amazon For a long time, "scaling" in foundation models mostly meant one thing: spend more compute on pre-training and capabilities rise.
• That intuition was supported by empirical work such as Kaplan et al.
• (2020) , which reported predictable power-law trends in loss as you scale model parameters , dataset size , and training compute .
• In practice, these trends justified sustained investment in large-scale accelerator capacity and the surrounding distributed infrastructure needed to keep it efficiently utilized.
• But the frontier has evolved—and scaling is no longer a single curve.
• NVIDIA's "from one to three scaling laws" framing usefully emphasizes that, beyond pre-training, performance increasingly scales through post-training (e.g., supervised fine-tuning (SFT) and reinforcement learning (RL)-based methods) and through test-time compute ("long thinking," search/verification, multi-sample strategies).