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QJL: 1-Bit Quantized JL Transform for KV Cache Quantization with Zero Overhead
Open accessAbstract
Serving LLMs requires substantial memory due to the storage requirements of Key-Value (KV) embeddings in the KV cache, which grows with sequence length. An effective approach to compress KV cache is quantization. However, traditional quantization methods face significant memory overhead due to the need to store quantization constants (at least a zero point and a scale) in full precision per data block. Depending on the block size, this overhead can add 1 or 2 bits per quantized number. We introduce QJL, a new quantization approach that consists of a Johnson-Lindenstrauss (JL) transform followed by sign-bit quantization. In contrast to existing methods, QJL eliminates memory overheads by removing the need for storing quantization constants. We propose an asymmetric estimator for the inner product of two vectors and demonstrate that applying QJL to one vector and a standard JL transform without quantization to the other provides an unbiased estimator with minimal distortion. We have developed an efficient implementation of the QJL sketch and its corresponding inner product estimator, incorporating a lightweight CUDA kernel for optimized computation. When applied across various LLMs and NLP tasks to quantize the KV cache to only 3 bits, QJL demonstrates a more than fivefold reduction in KV cache memory usage without compromising accuracy, all while achieving faster runtime.
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“KIVI (Liu et al. 2024) uses per-channel 2-bit keys and per-token values, while KVQuant(Hooper et al. 2024) and QJL (Zandieh, Daliri, and Han 2025) push to lower bit-widths with reduced overhead.”
From VarRate: Training-Free Variable-Rate KV Cache Compression for Long-Context LLMs (2026) · cites this paper as Zandieh, Daliri, and Han 2025
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Cited by
Sorted by citations
VarRate: Training-Free Variable-Rate KV Cache Compression for Long-Context LLMs
Esmat, S., Shah, D., & Jannesari, A. (2026) · arXiv · doi:10.48550/arxiv.2607.15498
Metadata
- Authors
- Zandieh, A., Daliri, M., & Han, I.
- Affiliations
- Google (United States) (US)
- New York University (US)
- Korea Advanced Institute of Science and Technology (KR)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2025-04-11
- Volume
- 39
- Issue
- 24
- Pages
- 25805-25813
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W4409363627
- Cited by
- 4
- References
- 1
- Citations by year
- 2026: 2 · 2025: 2
- FWCI
- 3.02
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- KV cache quantization, Johnson-Lindenstrauss transform, 1-bit quantization, large language model inference
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- CoChat. (2026). Citation check: QJL: 1-Bit Quantized JL Transform for KV Cache Quantization with Zero Overhead. https://app.cochat.ai/papers/10.1609/aaai.v39i24.34773
- The paper
- Zandieh, A., Daliri, M., & Han, I. (2025). QJL: 1-Bit Quantized JL Transform for KV Cache Quantization with Zero Overhead. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v39i24.34773
