CUBO: Self-Contained Retrieval-Augmented Generation on Consumer Laptops 10 GB Corpora, 16 GB RAM, Single-Device Deployment

TL;DR

CUBO is a system that enables retrieval-augmented generation on consumer laptops with limited RAM, integrating streaming, hybrid retrieval, and hardware-aware orchestration to achieve competitive performance while maintaining data privacy.

Contribution

The paper introduces CUBO, a novel RAG platform optimized for consumer laptops with 16 GB RAM, combining streaming ingestion, hybrid retrieval, and hardware-aware orchestration.

Findings

Achieves Recall@10 of 0.48-0.97 across BEIR domains.

Retrieval latency of 185 ms (p50) on standard laptops.

Operates within a 15.5 GB RAM limit while maintaining data privacy.

Abstract

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.