Layer Normalization

Layer Normalization is an alternative to batch normalization that normalizes across the features within each individual example rather than across the batch dimension. Introduced by Ba, Kiros, and Hinton in 2016, it works well even with small batches or variable-length sequences — making it the natural fit for transformer architectures. Modern large language models like GPT-4, Llama, and Gemini all use layer normalization (or its close cousin RMSNorm) inside every transformer block. Two main placement strategies exist: pre-norm (normalize before attention/FFN, used in most modern LLMs) and post-norm (normalize after, used in the original transformer). PyTorch implements it as nn.LayerNorm. Layer normalization appears throughout modern AI architectures and shows up in model documentation reviewed during AI governance, AI compliance, and AI risk management — particularly when teams port models between frameworks or convert them to deployment formats like ONNX or TensorRT.

Centralpoint Layers Governance Onto Modern AI: Layer normalisation lives inside transformers — Centralpoint sits cleanly above them. Oxcyon's Centralpoint AI Governance Platform is model-agnostic (ChatGPT, Gemini, Llama, embedded), meters every LLM interaction, keeps prompts and skills on-prem, and embeds chatbots wherever needed with a single JavaScript line.


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