luce-kvflash

# Luce KVFlash

**Luce KVFlash** is a [[concepts/vram-optimization|memory optimization]] technique enabling efficient execution of [[concepts/llm]] with long contexts on resource-constrained hardware, specifically small GPUs. It addresses [[concepts/memory|memory]] bottlenecks by implementing paging [[concepts/causes|mechanisms]] for the [[concepts/inference-optimization]], allowing models to handle extended [[concepts/context-windows|context windows]] (up to 256K [[concepts/tokens|tokens]]) without requiring excessive [[concepts/vram|VRAM]].

## Key Features
- **[[concepts/kv-cache-paging|KV Cache Paging]]**: Optimizes memory usage by managing the key-value cache more efficiently, preventing out-of-memory errors during [[concepts/200k-token-context-window|long-context processing]].
- **Small GPU Compatibility**: Designed to run on hardware with limited video memory, expanding [[concepts/accessibility|accessibility]] for [[concepts/local-ai|local LLM deployment]].
- **Long-Context Support**: Demonstrated capability to fit and process 256K token contexts effectively.

## Sources & References
- [[lab-notes/2026-06-15-Luce-KVFlash-Efficient-Long-Context-LLMs-via-KV-Cache-Pa|Luce KVFlash: Efficient Long-Context LLMs via KV Cache Paging on Small GPUs]]
- [Luce KVFlash: Fit 256K Context on a Small GPU - Local Hands-On Guide](https://www.youtube.com/watch?v=8rTVCRWvRDo) by [[entities/fahd-mirza|Fahd Mirza]]