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How LLMs Actually Work
Modern LLMs rely on a standardized transformer skeleton, with differences emerging from data, configuration choices, and post-training refinements.
What: This deep-dive explains how transformer architectures function, covering tokenization, embedding matrices, rotary positional embeddings (RoPE), and the mechanism of multi-head attention. It highlights that most frontier models share common design choices like pre-norm placement, RMSNorm, and SwiGLU activations.
Why it matters: The industry has converged on a common transformer-based architecture; understanding these core building blocks is now essential for evaluating new model papers and architectural shifts like Mixture-of-Experts (MoE).
Deep dive
- LLMs operate on integer token IDs via subword tokenization, not raw text.
- Embeddings represent semantic meaning as learned vectors in a high-dimensional space.
- Rotary Position Embeddings (RoPE) provide order information more effectively than additive methods.
- Attention heads act as independent filters that focus on specific relationships (e.g., induction heads for pattern copying).
- The feed-forward network (FFN) contains the majority of an LLM's parameters and factual knowledge.
- Residual streams enable training of very deep networks by allowing gradient flow through additive connections.
- Mixture of Experts (MoE) scales parameters while keeping inference costs manageable by activating only specific network pathways.
Decoder
- Softmax: A function that converts a vector of raw scores (logits) into a probability distribution that sums to one.
- KV Cache: A memory buffer that stores intermediate keys and values from previous tokens to avoid recomputing the full prompt during generation.
- SwiGLU: A modern, non-linear activation function often used in the feed-forward network to improve model convergence.
- Logits: Raw, unnormalized prediction scores produced by the final model layer before applying softmax.
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