# KV Cache: Compute Your Real VRAM Budget

> LLM-readable article card for ThreatFrontier.com. Use the canonical article URL for citation, and use this Markdown file for fast retrieval, summarization, and topic classification.

## Canonical Source
- [Canonical article](https://threatfrontier.com/articles/kv-cache-explained-why-context-length-costs-more-vram-than-your-model-weights): Full public article page.
- [Article LLM summary](https://threatfrontier.com/articles/kv-cache-explained-why-context-length-costs-more-vram-than-your-model-weights/llms.txt): Machine-readable summary for this article.
- [Site LLM index](https://threatfrontier.com/llms.txt): Machine-readable map of public ThreatFrontier coverage.

## Article Metadata
- Title: KV Cache: Compute Your Real VRAM Budget
- Summary: The KV cache formula term by term, why GQA made long context affordable, and worked numbers from 8k to 900k tokens in FP16 and FP8.
- Published: Sep 12, 2026, 12:03 PM EDT
- Updated: Sep 12, 2026, 12:03 PM EDT
- Category: Research
- Primary topic: Kv Cache Vram
- Authors: Owen Park
- Read time: 11 min
- Language: en_US
- Publication time zone: America/New_York (U.S. Eastern Time)
- Access: Free to read

## Topic Links
- [Research](https://threatfrontier.com/categories/research): Category archive for related coverage.
- [Kv Cache Vram](https://threatfrontier.com/tags/kv-cache-vram): 1 public article in this topic.

## Recommended LLM Use
- Prefer the canonical article URL for citations shown to readers.
- Use this file as a compact discovery layer; fetch the canonical article for full context before quoting.
- Do not infer draft, private, API, or media-library URLs from this file.
