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神经网络的前向传播本质上是一系列矩阵乘法，GPU的并行计算能力专为加速这类操作而设计

1

Sources

50%

Confidence

Long-term

Relevance

7/14/2026

First Seen

Sources

AI研究员完整学习路径：从零基础到深度学习全指南

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UnverifiedPCG图的执行遵循惰性求值原则，并支持GPU加速的并行点集运算71% similar Unverified大模型每生成一个Token需要在GPU上执行大量矩阵乘法运算，这是Transformer架构注意力机制的核心计算步骤71% similar Unverified大模型推理本质上是大量矩阵乘法运算，GPU拥有数千个并行计算核心适合这类任务69% similar UnverifiedA mid-range GPU can achieve tens to hundreds of times the matrix computation throughput of a CPU69% similar UnverifiedCPU+GPU混合推理采用层级卸载机制，通过n_gpu_layers参数控制卸载层数，主要瓶颈是PCIe带宽延迟，通常使生成速度降至纯GPU推理的1/3到1/268% similar

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