Unverified60% confidenceFactExact time
RNN must process inputs sequentially by time step and cannot fully utilize the parallel computing capabilities of modern GPUs, which is the fundamental reason Transformer replaced it.
2
Sources
60%
Confidence
Long-term
Relevance
7/20/2026
First Seen
Sources
Related Claims
UnverifiedMainstream Transformer architectures avoid redundant computation of already-processed tokens through the KV Cache mechanism.70% similarUnverified相比RNN/LSTM架构，Transformer支持大规模并行计算70% similarUnverifiedTransformer autoregressive generation is divided into the Prefill stage (compute-intensive, with higher GPU utilization) and the Decode stage (bandwidth-intensive, generating tokens one at a time)70% similarUnverified现代商用NPU执行Transformer模型推理时的能效比可达GPU的5至10倍70% similar
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