Scaling Laws for Familial Models
Published:Dec 29, 2025 12:01
•1 min read
•ArXiv
Analysis
This paper extends the concept of scaling laws, crucial for optimizing large language models (LLMs), to 'Familial models'. These models are designed for heterogeneous environments (edge-cloud) and utilize early exits and relay-style inference to deploy multiple sub-models from a single backbone. The research introduces 'Granularity (G)' as a new scaling variable alongside model size (N) and training tokens (D), aiming to understand how deployment flexibility impacts compute-optimality. The study's significance lies in its potential to validate the 'train once, deploy many' paradigm, which is vital for efficient resource utilization in diverse computing environments.
Key Takeaways
- •Introduces Granularity (G) as a new scaling variable for Familial models.
- •Proposes a unified scaling law L(N, D, G) to capture the relationship between model size, training data, and granularity.
- •Empirically validates the 'train once, deploy many' paradigm.
- •Demonstrates that deployment flexibility is achievable without compromising compute-optimality.
Reference
“The granularity penalty follows a multiplicative power law with an extremely small exponent.”