GQLA: Group-Query Latent Attention for Hardware-Adaptive Decoding
Decoding efficiency depends on KV-cache traffic, computation, and parallelism. Multi-head Latent Attention (MLA) offers absorbed and head-expanded forms, but its head-specific projections limit grouped KV sharing. We introduce Group-Query Latent Attention (GQLA), whose group-shared up-projections preserve a joint latent and enable a shardable expanded cache. The same weights support equivalent MQA-absorb and GQA execution using existing kernels. Roofline analysis relates path preference to GPU compute-to-bandwidth ratio and tensor parallelism (TP): absorption trades traffic for computation and replicates the latent across ranks; GQA shards KV groups. Pretraining with eight query heads per...
| Authors | Fanxu Meng, Zhixin Pan |
|---|---|
| Primary category | cs.LG |
| Published | 14 May 2026 |
| Last updated at source | 14 October 2026 |
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