GLIDE: Guided Layerwise Hybrid Attention for Efficient LLM Inference
As Large Language Models scale to increasingly long contexts, the memory I/O and computational overhead of the Key-Value (KV) cache during decoding emerges as the primary throughput bottleneck. To address this, we propose GLIDE, a Guided Layerwise Hybrid Attention that strategically integrates sliding-window softmax attention with linear recurrent aggregation. GLIDE is motivated by layer-wise heterogeneity: early layers exhibit high sensitivity to softmax removal, while deeper layers demonstrate redundancy and tolerate aggressive replacement by linear alternatives. Leveraging this insight, GLIDE introduces a layer-wise adaptive mechanism wherein each layer balances an efficient linear rec...
| Authors | Vimal William, Ravi Tandon, Jyotikrishna Dass |
|---|---|
| Primary category | cs.AI |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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