[2603.02188] Multi-Head Low-Rank Attention

Computer Science > Machine Learning arXiv:2603.02188 (cs) [Submitted on 2 Mar 2026] Title:Multi-Head Low-Rank Attention Authors:Songtao Liu, Hongwu Peng, Zhiwei Zhang, Zhengyu Chen, Yue Guo View a PDF of the paper titled Multi-Head Low-Rank Attention, by Songtao Liu and Hongwu Peng and Zhiwei Zhang and Zhengyu Chen and Yue Guo View PDF HTML (experimental) Abstract:Long-context inference in large language models is bottlenecked by Key--Value (KV) cache loading during the decoding stage, where the sequential nature of generation requires repeatedly transferring the KV cache from off-chip High-Bandwidth Memory (HBM) to on-chip Static Random-Access Memory (SRAM) at each step. While Multi-Head Latent Attention (MLA) significantly reduces the total KV cache size, it suffers from a sharding bottleneck during distributed decoding via Tensor Parallelism (TP). Since its single latent head cannot be partitioned, each device is forced to redundantly load the complete KV cache for every token, consuming excessive memory traffic and diminishing TP benefits like weight sharding. In this work, we propose Multi-Head Low-Rank Attention (MLRA), which enables partitionable latent states for efficient 4-way TP decoding. Extensive experiments show that MLRA achieves state-of-the-art perplexity and downstream task performance, while also delivering a 2.8$\times$ decoding speedup over MLA. Code is available at this https URL. Pretrained weights, along with the training and evaluation data, are av...