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LLM Architecture From Scratch: The Building Blocks That Matter
A clear, technical walk-through of modern LLM architecture, from tokenization and attention to training loops and inference trade-offs.
Why LLM Architecture Matters
Large language models are not just bigger neural networks. Their performance depends on architecture decisions that determine how context is represented, how long-range relationships are modeled, and how efficiently the model can be trained and served.
If you are building AI products, understanding these building blocks helps you make better choices about cost, latency, and quality. Architecture is the difference between a demo and a production system.
Tokenization Is the First Design Decision
LLMs operate on tokens, not raw text. The tokenization strategy affects vocabulary size, memory usage, and how well the model handles specialized terms.
Subword tokenizers like BPE and Unigram balance flexibility with compression. For domain-heavy apps, custom vocabulary can reduce token counts and improve accuracy.
Attention Is the Core Mechanism
Self-attention lets the model weigh relationships across a sequence. It is powerful, but it scales quadratically with context length.
This trade-off explains why long-context models use architecture tricks: sparse attention, sliding windows, or hybrid memory layers to preserve accuracy without exploding compute costs.
Feedforward Layers Carry Most of the Parameters
In transformer blocks, feedforward layers often hold more parameters than attention. They handle non-linear transformations that turn context into useful representation.
Tuning width, depth, and activation functions is not just academic. It changes compute budgets and how well the model generalizes to new tasks.
Training Loops and Data Shape the Model
Pretraining creates general language capability, while fine-tuning and alignment steer behavior. The dataset composition is just as important as the model size.
High-quality, well-balanced data often outperforms brute-force scale. For real products, curated data can be a competitive advantage.
Inference Trade-Offs in the Real World
Serving LLMs requires optimization: batching, quantization, and caching. These reduce cost and latency but can affect output quality.
Most production systems strike a balance. They use smaller models for routine tasks and route harder requests to larger, slower models.
FAQ: LLM Architecture
What is the biggest bottleneck in LLMs? Context length and attention scaling often dominate compute cost.
Do more parameters always mean better quality? No. Data quality, training strategy, and architecture matter just as much.
Is tokenization still important with large models? Yes. It affects cost, speed, and domain accuracy.
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