Scaling Laws for LLMs
Edition #137 | May 14, 2025
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Hello!!
Welcome to the new edition of Business Analytics Review!
Today, we’re exploring scaling laws for large language models (LLMs)—the empirical relationships that reveal how performance improves as we adjust model size, dataset scale, and compute investment.
Understanding Scaling Laws in LLMs
Scaling laws are empirical relationships that describe how the performance of machine learning models improves with increased resources. In the context of LLMs (Large Language Models), these resources refer to:
Model size (number of parameters),
Dataset size (total number of training tokens), and
Compute power (FLOPs or GPU hours).
The central insight from scaling laws is that there is a predictable, power-law relationship between these three variables and model performance (typically measured in cross-entropy loss or accuracy). This enables researchers to plan and budget for future models more effectively, ensuring a balanced investment between scale and return.
Before scaling laws, model development was more heuristic. Today, these laws act as a GPS guiding the LLM roadmap. Whether building a 1B, 10B, or 100B parameter model, the right amount of data and compute can be estimated with surprising accuracy.
Kaplan et al.'s 2020 Scaling Laws: The Origin Story
In a landmark 2020 paper titled "Scaling Laws for Neural Language Models," Jared Kaplan and colleagues (at OpenAI) investigated how training loss behaves as model size, dataset size, and compute are scaled up. They introduced three key scaling laws:
Model Size Scaling: As the number of parameters increases, the model's performance improves following a power-law curve until it hits an underfitting regime.
Data Scaling: More data leads to better performance until the model reaches a point of data saturation (overfitting if not scaled with model size).
Compute-Optimal Scaling: There's an optimal frontier where compute, model size, and dataset size are in harmony. Straying from this line yields inefficient use of resources.
These scaling laws suggested that there were no fundamental barriers to better LLM performance—just more compute and better scaling.
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Chinchilla Scaling Laws: Rethinking Size vs Data
In 2022, DeepMind published a follow-up study titled "Training Compute-Optimal Large Language Models," often dubbed the "Chinchilla paper." They challenged Kaplan's assumption that data and compute scale equally. Instead, they found:
Many existing models (like GPT-3) were too big and trained on too little data.
For the same compute budget, smaller models trained on more data perform better than larger models trained on less.
This flipped the strategy. Instead of scaling only parameters, data quantity and diversity gained equal priority. Chinchilla's findings led to more efficient models that were cheaper to deploy, required less energy, and were easier to train.
Read the paper: Hoffmann et al., 2022 - Training Compute-Optimal LLMs
Real-World Implications
Why do scaling laws matter to practitioners and business leaders?
Predictability: They allow AI teams to estimate how much data and compute are needed to reach a target performance.
Budget Planning: Helps organizations allocate resources better between compute, engineering, and data acquisition.
Strategic Choices: Provides clarity on whether to invest in model size vs training duration vs better data.
Environmental Considerations: Over-scaling leads to unnecessary energy consumption. Efficient scaling is more eco-conscious.
Innovation Catalyst: Scaling laws fuel innovation in architecture (e.g., sparse models, low-rank adapters) by highlighting where standard dense models hit diminishing returns.
From GPT-3 to Gemini and Claude to Mistral, these laws have shaped every frontier AI advancement.
Recommended Reads
Scaling Laws for Neural Language Models (Kaplan et al., 2020)
Training Compute-Optimal Large Language Models (Hoffmann et al., 2022)
Resolving Discrepancies in Compute-Optimal Scaling (Porian et al., 2024)
Compute-Optimal LLMs Provably Generalize Better With Scale (Finzi et al., 2025)
Demystify Transformers: A Guide to Scaling Laws (Yu-Cheng Tsai, Medium)
Trending in AI and Data Science
Let’s catch up on some of the latest happenings in the world of AI and Data Science:
Perplexity AI Eyes $1.4B Valuation
Perplexity AI is reportedly in talks for a funding round that could raise its valuation to $1.4 billion, reflecting strong investor interest in AI-driven search innovation.Saudi Arabia Launches State-Backed AI Firm
Saudi Arabia’s Crown Prince launched “Humain” under the Public Investment Fund to develop AI technologies, including advanced data centers and Arabic language models, aiming to make the kingdom a global AI hub.US Considers AI Chip Sale to UAE’s G42
The Trump administration is considering a major sale of U.S. AI chips to UAE’s G42, a move that could reshape global AI leadership but raises concerns about technology transfer and national security.
Trending AI Tool: Weights & Biases (WandB)
Weights & Biases is a powerful platform used by leading ML practitioners to track experiments, visualize results, and collaborate across teams. It’s especially helpful when training large language models or experimenting with scaling laws.
Key Features:
Experiment Tracking: Log hyperparameters, loss curves, and metrics across training runs in real-time.
Visual Dashboards: Create customizable reports to visualize model performance, scaling trends, and training efficiency.
Version Control: Track datasets, models, and code versions with robust lineage support.
Collaborative Reports: Share results with teams or the research community via live, interactive reports.
Scalability: Easily integrates with PyTorch, TensorFlow, HuggingFace Transformers, and JAX for large-scale model training.
Until next time, keep scaling your insights and building smarter models! Explore our partnership opportunities here
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Great post! The breakdown of scaling laws, especially the comparison between Kaplan et al.'s findings and the Chinchilla paper, is super insightful. Understanding the balance between model size, data, and compute is crucial for efficient LLM development.
Thanks for sharing the recommended reads as well.