# Sparse Attention & Efficiency in Long-Context LLMs

Category: Retrieval & Pipelines
Stack: FlashAttention, Subquadratic Attention, State-Space Models

## Overview
Understanding O(N^2) memory bottlenecks in 1M+ token windows and selecting subquadratic architectures for enterprise ingestion.

## The Problem
Standard quadratic self-attention scales quadratically with context length, creating prohibitive GPU VRAM costs and latency degradation during large-document processing.

## Production Rule
Evaluate subquadratic attention layers (such as state-space models and block-sparse attention) when building high-throughput document processing pipelines exceeding 128k context.

## Code / Specification
File: `attention_complexity.md`

```model architecture
Full Self-Attention: O(N^2) time & memory
Block-Sparse / Subquadratic: O(N * sqrt(N)) or O(N log N)
State-Space / Recurrent Models: O(N) linear time during inference

Pipeline guideline:
1. Use 1M+ token context windows for low-frequency synthesis tasks.
2. Use chunked vector index + reranking for high-concurrency Q&A to avoid quadratic VRAM spikes.
```