---
id: "prereq-llm-transformer-architecture"
type: "prereq"
source_timestamps: ["09:27:00", "17:20:00"]
tags: ["machine-learning", "fundamentals"]
related: ["concept-kv-cache", "concept-multi-head-latent-attention"]
reason: "Required to understand why the KV cache is necessary and why it grows linearly with context."
sources: ["s49-killed-ram-limits"]
sourceVaultSlug: "s49-killed-ram-limits"
originDay: 49
---
# Understanding of Transformer Architecture and Attention

**Prerequisite**: Understanding of Transformer Architecture and Attention.

**Why**: To fully grasp why the [[concept-kv-cache]] exists and why it becomes a bottleneck, one must understand:

1. How **autoregressive transformer models** generate text one token at a time.
2. How the **attention mechanism** requires access to all previous tokens to maintain context.
3. Why caching the keys and values from prior tokens avoids recomputing them on every step (a quadratic-to-linear optimization in token count).

Without this foundation, the motivation for storing key-value pairs and the linear growth of memory with context length is not intuitive. This prerequisite is essential for engaging with:
- [[concept-kv-cache]]
- [[concept-multi-head-latent-attention]]
- [[concept-polar-quantization]]
- [[framework-memory-optimization-landscape]]