# Revisiting RaBitQ and TurboQuant: A Symmetric Comparison of Methods, Theory, and Experiments

arXiv: https://arxiv.org/abs/2604.19528 · HTML: https://mlsanity.com/paper/2604.19528/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 21 Apr 2026.

## Builds on (most cited first, 11 of 11)

- [LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding](https://mlsanity.com/paper/2308.14508/) (2023)
- [TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate](https://mlsanity.com/paper/2504.19874/) (2025)
- [Practical and Optimal LSH for Angular Distance](https://mlsanity.com/paper/1509.02897/) (2015)
- [EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning](https://mlsanity.com/paper/2108.08842/) (2021)
- [DRIVE: One-bit Distributed Mean Estimation](https://mlsanity.com/paper/2105.08339/) (2021)
- [QJL: 1-Bit Quantized JL Transform for KV Cache Quantization with Zero Overhead](https://mlsanity.com/paper/2406.03482/) (2024)
- [RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2405.12497/) (2024)
- [Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2409.09913/) (2024)
- [Accelerated Nearest Neighbor Search with Quick ADC](https://mlsanity.com/paper/1704.07355/) (2017)
- [GPU-Native Approximate Nearest Neighbor Search with IVF-RaBitQ: Fast Index Build and Search](https://mlsanity.com/paper/2602.23999/) (2026)
- [Token Sparse Attention: Efficient Long-Context Inference with Interleaved Token Selection](https://mlsanity.com/paper/2602.03216/) (2026)

## Built on by (most cited first, 3 of 3)

- [FibQuant: Universal Vector Quantization for Random-Access KV-Cache Compression](https://mlsanity.com/paper/2605.11478/) (2026)
- [Block-Sphere Vector Quantization](https://mlsanity.com/paper/2605.19972/) (2026)
- [Fast-TurboQuant: A Multiplier-Free Online Vector Quantization Approach](https://mlsanity.com/paper/2606.21448/) (2026)

## Abstract (arXiv)

This technical note revisits the relationship between RaBitQ and TurboQuant under a unified comparison framework. We compare the two methods in terms of methodology, theoretical guarantees, and empirical performance, using a reproducible, transparent, and symmetric setup. Our results show that, despite the claimed advantage of TurboQuant, TurboQuant performs worse than RaBitQ in most tested settings of inner-product estimation, nearest-neighbor search and KV cache quantization. We further find that several reported runtime and recall results in the TurboQuant paper could not be reproduced from the released implementation under the stated configuration. Overall, this note clarifies the shared structure and genuine differences between the two lines of work, while documenting reproducibility issues in the experimental results reported by the TurboQuant paper.