SANTA sparsifies post-softmax value aggregation via stratified sampling of S << n_k indices to produce an unbiased estimator, delivering 1.5x decode attention speedup on RTX 6000 Ada at 32k contexts while matching baseline accuracy.
Gqa: Training generalized multi-query transformer models from multi-head checkpoints
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Stochastic Sparse Attention for Memory-Bound Inference
SANTA sparsifies post-softmax value aggregation via stratified sampling of S << n_k indices to produce an unbiased estimator, delivering 1.5x decode attention speedup on RTX 6000 Ada at 32k contexts while matching baseline accuracy.