The second approach offers broader feature support, seen in projects like Cloud Hypervisor or QEMU microvm. Built for heavier and more dynamic workloads, it supports hot-plugging memory and CPUs, which is useful for dynamic build runners that need to scale up during compilation. It also supports GPU passthrough, which is essential for AI workloads, while still maintaining the fast boot times of a microVM.
GLU/SwiGLU 在实际中是门控形式(two linear branches),是向量上的逐元素操作;为了在一维上可视化,我用简化的标量形式来画图 —— 把两条分支都用相同的输入值(即把 a=x, b=x),因此 GLU(x)=x∗sigmoid(x) SwiGLU(x)=x∗SiLU(x) 。这能直观展示门控机制的形状差异。
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Editor: Martin Smith