Scaling Laws for Familial Models
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 / Citation
View Original"The granularity penalty follows a multiplicative power law with an extremely small exponent."