Unverified50% confidenceFactExact time
RWKV通过线性注意力加时间衰减的WKV算子实现线性递归架构效果
1
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50%
Confidence
Long-term
Relevance
8/24/2026
First Seen
Sources
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Related Claims
UnverifiedTransformer 架构中自注意力层会为每个 Token 生成对应的 Key 和 Value 向量并缓存在 GPU 显存中，即 KV Cache 机制，上下文越长占用显存越大，推理成本呈近线性增长64% similarUnverified张量并行通过 All-Reduce 操作在所有参与 GPU 之间同步中间激活值，通信量随并行度线性增长63% similarUnverifiedGPU并行归约操作的浮点累加顺序不固定导致传统Transformer注意力机制的实际输出存在微小的不可复现波动63% similarUnverifiedIn traditional Transformer architectures, as sequence length increases, both floating-point operations for attention computation and KV Cache memory usage grow linearly or even super-linearly.63% similar
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