How LLMs Actually Work

How LLMs Actually Work: A Technical Walkthrough

Core LLM Pipeline Stages

• The process involves nine key stages: Tokenization $\to$ Embeddings $\to$ Positional Encoding $\to$ Attention $\to$ Multi-head Attention $\to$ Feed-forward Network $\to$ Residual Stream/Layer Normalization $\to$ Next Token Prediction.
• The model operates on a sequence of integers (token IDs) rather than raw text.

Tokenization

• Converts input text into a sequence of integer IDs using a fixed vocabulary.
• Modern models use subword tokenization (e.g., Byte Pair Encoding) for efficiency and generalization.
• The choice of tokenizer impacts compute cost and multilingual coverage.

Embeddings & Positional Encoding

• Token IDs are mapped to dense vectors (embeddings) representing semantic meaning.
• Positional encoding injects sequence order information. Modern models often use Rotary Position Embeddings (RoPE) for better generalization to longer contexts.
• The combination of embedding and positional encoding gives the model both meaning and sequence context.

Attention Mechanism (The Core)

• Attention allows each token to weigh the importance of all other tokens in the sequence.
• Tokens are transformed into Query (Q), Key (K), and Value (V) vectors.
• Match scores are calculated via scaled dot product between Q and K, followed by Softmax to derive weights.
• The output is a weighted average of the Value vectors, allowing information flow across distances.

Multi-Head Attention & Parallelism

• Running attention in parallel across multiple ‘heads’ allows the model to learn diverse relationships (e.g., grammar, coreference, positional tracking).
• Different heads specialize in different roles, leading to emergent capabilities like ‘induction heads’ that track patterns.
• Modern optimizations like Grouped-Query Attention (GQA) reduce memory overhead (KV cache) while maintaining performance.

Feed-Forward Network (FFN) & Memory

• The FFN processes each token vector independently, performing non-linear transformations (e.g., using SwiGLU) to encode deeper structure.
• A significant portion of the model’s factual and semantic knowledge is stored within the FFN weights.
• Advanced scaling techniques like Mixture of Experts (MoE) use parallel FFNs (experts) to increase parameter count while keeping per-token compute cost manageable.

Training & Inference Loop

• The Residual Stream accumulates contributions from each layer, while Layer Normalization keeps the vector magnitudes stable during training.
• Next-token prediction is the objective: the model predicts the next token based on the final layer’s vector, using logits and a temperature/sampling strategy.
• Inference relies on the KV cache to avoid recomputing past tokens, making generation sequential.

Key Takeaways

• LLMs are highly complex, deep stacks of transformer blocks, each layer refining the token’s vector through attention and FFNs.
• The convergence on specific components (RoPE, RMSNorm, SwiGLU) reflects a mature, optimized design pattern for modern frontier models.
• The model’s function is fundamentally next-token prediction, which enables emergent capabilities like reasoning and coding.

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Topic: AI Infrastructure
Tags: LLM Transformer DeepLearning ML