A Plain English Breakdown of Transformer Mechanics
Zulfikar_Ramzan · x · 2026-07-18
This long-form article breaks down the key components of Transformer—the core technological turning point of the GPT era—in an accessible way.
The piece covers:
- token and tokenizer
- matrix multiplication, softmax
- KV cache
- RoPE (Rotary Positional Embedding)
- transformer block and attention
- differences between encoder / decoder
- pre-training, fine-tuning, reinforcement learning
- MoE (Mixture of Experts)
- the "memory bill" during inference
The overall goal is to use straightforward explanations to help readers understand why large models work, why they are expensive, and what specific engineering problems these components solve.
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