Bound for a random matrix and a normal distributed random vector

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In the topic of Principal component analysis in high dimensions I have given the following task:

Let $X\in\mathbb R^{n\times d}$ and $w\sim\mathcal N(0,\sigma^2I_n)$.

Show that for any $\lambda>0$ we have: $$\mathbb P\left(\bigg\|\frac{X^T w}n\bigg\|_\infty\geq\lambda\right)\leq2e^{-\frac{n\lambda^2}{2\sigma^2}+\log(d)}$$

can someone give me a hint?