Bottom line: Foundation model development today scales across three channels: pre-training, post-training, and test-time compute. AWS shows how its infrastructure—accelerators, networking, storage—works together with open-source tools like PyTorch, Kubernetes and Prometheus to enable efficient training and inference.
AWS analyzes the modern requirements for training and inference of foundation models. Scaling now extends across pre-training, post-training and test-time compute—and requires tight coordination of accelerator infrastructure, networking and storage.
For a long time, “scaling” in foundation models meant primarily one thing: applying more compute power in pre-training to achieve better results. This intuition was supported by empirical work such as Kaplan et al. (2020), which showed that model parameters, dataset size and training effort follow computable power laws.
Today the frontier has shifted: scaling no longer follows a single path. NVIDIA’s concept of “three scaling laws” emphasizes that performance grows alongside pre-training increasingly through post-training (supervised fine-tuning, RL-based methods) and test-time compute (long thinking, search procedures, multi-sample strategies).
These different scaling regimes—pre-training, post-training and inference—converge on infrastructure requirements: tightly coupled accelerator compute, high-bandwidth networking with low latency, and distributed storage backend. At the same time, resource orchestration and hardware observability become critical to ensure cluster health and diagnose performance issues.
Another trend is growing reliance on the open-source ecosystem—from model frameworks through resource management to operational tools. At the cluster level, systems like Slurm and Kubernetes manage resources. Model development and distributed training often use PyTorch and JAX. For observability, organizations rely on Prometheus (metrics) and Grafana (visualization and alerting).
AWS provides a detailed analysis of how its multi-node accelerator infrastructure, high-bandwidth networking, distributed storage and managed services interact with common OSS stacks.