Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws
by Databricks / CMU · free · Last verified 2026-03-17
Extends Chinchilla scaling laws by incorporating inference costs into the compute-optimal analysis. Shows that when inference demand is high, training smaller models on more tokens is suboptimal — real deployments should use larger models trained on fewer tokens than Chinchilla suggests.
https://arxiv.org/abs/2401.00448 ↗C+
C+—Average
Adoption: C+Quality: AFreshness: ACitations: CEngagement: F
Specifications
- License
- Open Access
- Pricing
- free
- Capabilities
- scaling-analysis, inference-cost-modeling, deployment-optimization
- Integrations
- Use Cases
- deployment-planning, cost-optimization, model-selection
- API Available
- No
- Tags
- scaling-laws, inference-cost, chinchilla, compute-optimal, deployment, total-cost
- Added
- 2026-03-17
- Completeness
- 100%
Index Score
52.4Adoption
58
Quality
86
Freshness
82
Citations
48
Engagement
0