让16GB内存笔记本在本地跑大模型检索，符合GDPR要求
CUBO: Self-Contained Retrieval-Augmented Generation on Consumer Laptops 10 GB Corpora, 16 GB RAM, Single-Device Deployment
- 用流式处理+分层检索+硬件感知调度，实现低内存高效率
- 在BEIR数据集上召回率0.48-0.97，延迟仅185毫秒（中位数）
- 适合需本地部署、合规性强的中小企业文档系统
处理敏感文档的机构面临困境：云端AI可能违反GDPR，而本地部署通常需18-32GB内存。本文提出CUBO，一个面向消费级笔记本（16GB共享内存）的系统级RAG平台。其创新在于工程集成的流式摄入（缓冲开销O(1)）、分层混合检索与硬件感知调度，可在严格15.5GB内存限制下实现媲美主流系统的召回率（BEIR各领域Recall@10为0.48-0.97）。3.7万行代码在C1,300台设备上实现185毫秒（中位数）检索延迟，且通过全程本地处理满足GDPR第5(1)(c)条的数据最小化要求。BEIR基准测试验证了其在中小型专业档案中的实用部署能力。代码已开源：https://github.com/PaoloAstrino/CUBO。
原文摘要 · Abstract (English)
Organizations handling sensitive documents face a tension: cloud-based AI risks GDPR violations, while local systems typically require 18-32 GB RAM. This paper presents CUBO, a systems-oriented RAG platform for consumer laptops with 16 GB shared memory. CUBO's novelty lies in engineering integration of streaming ingestion (O(1) buffer overhead), tiered hybrid retrieval, and hardware-aware orchestration that enables competitive Recall@10 (0.48-0.97 across BEIR domains) within a hard 15.5 GB RAM ceiling. The 37,000-line codebase achieves retrieval latencies of 185 ms (p50) on C1,300 laptops while maintaining data minimization through local-only processing aligned with GDPR Art. 5(1)(c). Evaluation on BEIR benchmarks validates practical deployability for small-to-medium professional archives. The codebase is publicly available at https://github.com/PaoloAstrino/CUBO.
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