Optimizing Clusters for Billion-Scale Quantization-Based NNS

A commutative vector quantization method for KV cache compression that reduces FP16 cache size by 87.5% with 2-bit quantization and enables 1-bit quantization, allowing LLaMA-3.1 8B to run with 128K context on a single RTX 4090 GPU.

FusionANNS architecture by Bing Tian et al. for billion-scale ANN search using CPU/GPU cooperation.

Highly-efficient billion-scale approximate nearest neighbor search algorithm introduced by Chen et al. Focuses on scalability and performance for large datasets in high-dimensional spaces. Relevant for vector database indexing techniques.

SIGKDD 2022 paper introducing BLISS, a billion-scale indexing method using iterative re-partitioning for large-scale approximate nearest neighbor search.

SIGKDD 2025 paper proposing a progressive distribution transformation approach for boosting deep vector quantization, improving quantization accuracy by progressively adapting data distributions during training.

VLDB 2022 paper introducing Hercules, an approach for efficient data series (time series) similarity search at scale, leveraging advanced indexing and pruning techniques for billion-scale sequence datasets.