Scaling laws describe the relationship between the size of language models and their capabilities. Unlike prior studies that evaluate a model’s capability via loss or benchmarks, we estimate the number of knowledgebitsa model stores. We focus on factual knowledge represented as tuples, such as (USA, capital, Washington D.C.)  from a Wikipedia page. Through multiple controlled datasets, we establish that language models can and only can store2 bits of knowledge per parameter, even when quantized to int8, and such knowledge can be flexibly extracted for downstream applications. Consequently, a 7B model can store 14B bits of knowledge, surpassing the English Wikipedia and textbooks combined based on our estimation. More broadly, we present 12 resultson how (1)  training duration, (2)  model architecture, (3)  quantization, (4)  sparsity constraints such as MoE, and (5)  data signal-to-noise ratio affect a model’s knowledge storage capacity. Notable insights include: The GPT-2 architecture, with rotary embedding, matches or even surpasses LLaMA/Mistral architecturesin knowledge storage, particularly over shorter training durations. This arises because LLaMA/Mistral uses GatedMLP, which is less stable and harder to train. Prepending training data with domain names (e.g., wikipedia.org)  significantly increases a model’s knowledge capacity. Language models can autonomously identify and prioritize domains rich in knowledge, optimizing their storage capacity.