Grouped Value Attention Reduces KV Cache Scalars by 45%
September 7, 2026
Grouped Value Attention (GVA) reduces Transformer decoding bottlenecks by storing grouped values and reconstructing content keys via a learned linear map. At a 350M-parameter scale, this method reduces persistent cache scalars by 45-47% compared to Grouped-query attention (GQA).
HOW THIS AFFECTS YOU
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builderYou can achieve significantly lower memory usage during long-sequence inference.
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researcherThis presents a new method for optimizing KV cache memory footprints.