TurboEmbed: An Idea About Zero-Loss Angular Quantization for High-Performance Vector Similarity Search

This paper introduces "TurboEmbed," a data-oblivious quantization framework designed to accelerate high-dimensional vector similarity searches in RAG and LLM systems. By leveraging the 360-degree angular normalization from the TurboQuant (2026) algorithm and QJL projections, TurboEmbed converts traditional floating-point cosine similarity into high-speed bitwise operations. Our implementation demonstrates a 6x-32x memory reduction and a 12x-20x theoretical throughput speedup with a correlation of >0.83 compared to FP32 baselines, enabling massive vector databases to run on consumer-grade hardware.