Scaling Laws: How to Allocate Compute for Training Language Models
Author(s): M
Originally published on Towards AI.
From Chinchilla’s 20:1 rule to SmolLM3’s 3,700:1 ratio: how inference economics rewrote the training playbook
Training a language model is expensive. Really expensive. A single training run for a 70 billion parameter model can cost millions of dollars in compute.
This article explores the concept of scaling laws in training language models, emphasizing the importance of balancing model size, training data, and compute budget. It discusses the findings from DeepMind’s Chinchilla research, which highlighted that models should scale equally in size and data for optimal performance. By following these empirical guidelines, practitioners can achieve significant improvements in model efficiency and effectiveness, ultimately leading to better-performing language models while addressing the critical trade-offs between training and inference costs.
Read the full blog for free on Medium.
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