Unverified50% confidenceFactExact time
GEMV can complete dequantization while reading the weights, whereas GEMM's tiling logic makes the dequantization process more complex.
1
Sources
50%
Confidence
Long-term
Relevance
10/11/2026
First Seen
Sources
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Related Claims
UnverifiedThe Decode stage produces only one token at a time, resembling GEMV in form, with extremely low arithmetic intensity and is memory-bound.71% similarUnverifiedThe two prefill kernels, GEMM and row-by-row GEMV, produced output that was identical in the first 597 characters under the same prompt and greedy decoding, then began to diverge.70% similarUnverifiedThe author added a fallback mechanism: splitting small-batch inputs into individual rows, running GEMV on each row separately, and then concatenating the results.64% similarUnverifiedThe computational form of GEMM can be described as C = A × B + C, where A, B, and C are all two-dimensional matrices.63% similarUnverifiedSageAttention improves arithmetic intensity through mixed-precision quantization, while FlashAttention reduces HBM read/write operations through tiling; the two optimize along different dimensions.61% similar
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