Explore why LLM scaling laws fail in real-world production. Learn about Chinchilla optimality, overtraining, and the limits of compute-driven AI growth.
Workload placement for LLMs isn't about using the biggest model-it's about matching tasks to the right hardware and infrastructure. Learn how to cut costs, avoid bottlenecks, and speed up training and inference by placing workloads smarter.