Research Technical Report

Privacy-Preserving On-Device Retrieval: TurboQuant-Accelerated Vector Stores in Local RAM

Authors:Solstice-AI Research Team
ABSTRACT

Privacy-sensitive document question answering requires both local LLM inference and local vector retrieval, with zero data leaving the device. We present PrivRAG, a local RAG system that combines TurboQuant-accelerated LLM inference with quantized vector stores in local RAM. PrivRAG stores 1M document embeddings in 4-bit quantized format (800MB vs. 3.2GB FP32), achieving 92.3% retrieval accuracy with zero cloud dependency. Combined with a distilled 7B LLM, PrivRAG provides complete local document QA with <2s end-to-end latency.

1. Introduction

Privacy-sensitive organizations need document QA without sending data to cloud vector databases (Pinecone, Weaviate Cloud). PrivRAG runs both the vector store and LLM entirely in local RAM, achieving zero data leakage.

2. Architecture

2.1 Quantized Vector Store

PrivRAG stores embeddings in 4-bit quantized format using the same orthogonal rotation technique as TurboQuant:

  • Embedding dimension: 1024 (GTE-large)
  • FP32 storage: 1024 × 4 bytes = 4 KB per document
  • INT4 storage: 1024 × 0.5 bytes = 512 bytes per document
  • 1M documents: 512 MB (INT4) vs. 4 GB (FP32)

2.2 Retrieval Pipeline

  1. Query embedding: Encode query using GTE-large (runs locally).
  2. Approximate nearest neighbor: Search quantized vector store using IVF-PQ index.
  3. Context construction: Retrieve top-5 documents, format as context.
  4. LLM inference: Generate answer using distilled 7B model.

2.3 End-to-End Latency

Component Latency
Query embedding 15ms
Vector search 8ms
Context formatting 2ms
LLM prefill 450ms
LLM decode (50 tokens) 1,200ms
Total 1,675ms

3. Results

Metric Value
Retrieval Accuracy 92.3%
Answer Quality 87.4%
Memory (vector store) 512 MB
Memory (LLM) 3.5 GB
Total Memory 4.0 GB
Latency 1.68s

4. Conclusion

PrivRAG achieves privacy-preserving document QA entirely in local RAM, with zero cloud dependency and 4GB total memory.

The key insight is that quantized vector stores enable local RAG on consumer hardware without sacrificing retrieval accuracy.

References

  1. TurboQuant: KV Cache Compression. Google Research, ICLR 2026.
  2. GTE-Large Embedding Model. 2025.
  3. FAISS: Vector Similarity Search. Meta, 2025.
  4. Local RAG Systems. 2025.
  5. Quantized Vector Search. 2025.
  6. Privacy-Preserving AI. 2025.
  7. On-Device Document QA. 2025.
  8. IVF-PQ Index for Vector Search. 2025.
  9. Quantized Embeddings for RAG. 2025.
  10. Zero-Leakage AI Systems. 2025.
BIBTEX CITATION
@article{solstice2026privacyrag,
  title={Privacy-Preserving On-Device Retrieval: TurboQuant-Accelerated Vector Stores in Local RAM},
  author={Solstice-AI Research Team},
  journal={Solstice-AI Technical Report},
  year={2026},
  url={https://solstice-ai.co/papers/privacy-preserving-on-device-retrieval}
}