Qdrant TurboQuant Explained: Is TurboQuant the Silver Bullet?

VQ-bench: A Composable Vector Quantization Framework

arXiv:2608. 11240v1 Announce Type: new Abstract: Vector quantization is an old problem but has recently become central to AI infrastructure.

Leech Lattice Vector Quantization for Efficient LLM Compression

arXiv:2603. 11021v2 Announce Type: replace Abstract: Scalar quantization of large language models (LLMs) is fundamentally limited by information-theoretic bounds.

Quanto: a PyTorch quantization backend for Optimum

HeRo-Q: A General Framework for Stable Low Bit Quantization via Hessian Conditioning

arXiv:2601. 21626v2 Announce Type: replace-cross Abstract: Post Training Quantization (PTQ), a mainstream model compression technique, often leads to the paradoxical 'low error, high loss' phenomenon because it focuses solely on minimizing quantization error.

SSTQ:Privacy-Preserving Vector Quantization via Subsampled Stochastic TurboQuant

arXiv:2608. 05127v1 Announce Type: cross Abstract: Achieving local differential privacy in distributed optimization while maintaining low communication cost remains challenging.

Inner Product Aware Quantization: Provably Fast, Accurate, and Adaptive Algorithms

arXiv:2606. 00289v1 Announce Type: new Abstract: Quantization is a fundamental tool used to compress datasets, neural network weights, and memory usage in a range of computational tasks.

Price of metric universality in vector quantization is at most 0.11 bit

arXiv:2602. 05790v2 Announce Type: replace-cross Abstract: Fast computation of a matrix product $W^\top X$ is a workhorse of modern LLMs.

Break Through the Compression Bottleneck: From Theory to Practice

arXiv:2607. 20434v1 Announce Type: cross Abstract: As the parameter size of language models continues to grow, effective model compression is required to reduce their computational and memory overhead.

NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Models

arXiv:2602. 06694v3 Announce Type: replace Abstract: Weight-only quantization has become a standard approach for efficiently serving large language models (LLMs).

SandwichQuant: Which Parameters Matter Before and After Quantization?

The paper investigates which trainable parameters most influence quantization correction, finding that normalization-affine parameters form a low‑dimensional subspace that is highly effective for correction. It introduces SandwichQuant, a two‑stage framework that first adapts normalization-affine parameters before quantization to boost robustness, then fine‑tunes them after quantization to reduce residual errors. Experiments on vision and large language models show consistent gains across various low‑bit settings, confirming the benefit of subspace‑aligned correction.

WaterSIC: Information-Theoretically (Near) Optimal Linear Layer Quantization

arXiv:2603. 04956v2 Announce Type: replace Abstract: This paper considers the problem of converting a given dense linear layer to low precision.

TurboVec: A Case Study in Cost-Efficient Private Retrieval for Enterprise RAG via Codebook-Oblivious Quantization

arXiv:2607. 16973v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems increasingly power enterprise LLM applications, yet the vector retrieval layer introduces two underexplored challenges: (1) trained codebook quantizers may expose corpus statistics during index construction, creating a leakage channel in multi-tenant deployments, and (2) post-hoc filtering for tenant isolation degrades recall on selective queries.