LLM Architecture Explained: From Tokens to Text
A practical walkthrough of modern LLM architecture: tokenizer, transformer blocks, attention, training, and inference-time optimization.
1. Pipeline Overview
A large language model pipeline starts with tokenization, passes token IDs through stacked transformer layers, and ends with a probability distribution over the vocabulary for the next token.
At generation time, the model repeats this step autoregressively. Every produced token becomes part of the next input window, so latency and memory behavior become key architecture concerns.
2. Tokenization and Embeddings
Raw text is split into subword units with BPE or Unigram tokenization. These token IDs are mapped into dense vectors by the embedding table.
Positional information is injected so the model can understand order. Modern models use rotary position embeddings (RoPE) or similar techniques to improve long-context behavior.
3. Transformer Block Internals
Each block usually contains multi-head self-attention and a feed-forward network, wrapped with residual connections and normalization.
Self-attention lets each token weigh other tokens in context. Feed-forward layers then apply nonlinear feature transformations token-wise. Stacking many blocks gives the model depth and abstraction capacity.
4. Attention, KV Cache, and Context
In inference, key-value (KV) caching stores past attention projections so the model does not recompute them every step. This dramatically reduces generation cost for long outputs.
The context window is limited. Architectures differ in how they extend effective context, including sliding-window attention, grouped-query attention, and memory-efficient kernels.
5. Training Stack
Pretraining uses massive corpora and next-token prediction. Optimization usually combines AdamW-like optimizers, learning-rate schedules, mixed precision, and distributed parallelism.
After base pretraining, instruction tuning and preference alignment (for example through supervised fine-tuning and reinforcement learning variants) shape model behavior for assistant use cases.
6. Inference and Product Architecture
Real-world systems add routing, batching, prompt caching, guardrails, and observability around the base model. Most product quality gains come from this serving layer, not only from bigger models.
For engineering teams, architecture decisions should balance quality, latency, throughput, and cost. Better prompts and retrieval often beat blindly scaling parameters.