$k\sum v_i v_i^T-\big(\sum v_i\big)\big(\sum v_i^T\big)\succeq 0$

Indeed, the form of the $v_i$ is not needed, but we utilize the fact that $k$ is a positive integer: \begin{align} k\sum_{i=1}^k x^T v_i v_i^T x-x^T \left(\sum_{i=1}^k v_i\right)\left(\sum_{i=1}^k v_i^T \right)x &= \left(\sum_{i=1}^k 1\right) \left(\sum_{i=1}^k w_i^2\right) -\left(\sum_{i=1}^k w_i\right)^2,\textrm{ where }w_i\equiv x^Tv_i\\ &=\left\Vert1_k\right\Vert^2 \left\Vert w\right\Vert^2 - \left|\langle 1_k,w\rangle\right|^2, \textrm{ where }w\equiv(w_1,\dots,w_k)^T \\ &\geq ^{CS} \left|\langle 1_k,w\rangle\right|^2 - \left|\langle 1_k,w\rangle\right|^2\\ &=0\\ &\hspace{2.5in}\blacksquare \end{align}

I guess that the vectors are row vectors of an $\mathbb{R}^n$. If the vectors are column vectors, take transposes of the difference and then follow the proof below, to prove that this transpose is positive semi-definite. Then, since $A$ is positive semidefinite if-f $A^{T}$ is, the desired result follows in the case of column vectors too. In the case of row vectors the inequality is equivalent to proving that

$$k\sum_{i=1}^k||u_i||^2\geq \left|\left|\sum_{i=1}^ku_i\right|\right|^2.\ \ \ (1)$$

Indeed, note that for the difference in your question the matrix multiplication $x($this difference$)x^{T}$ is equal to

$$||x||^2\left(k\sum_{i=1}^k||u_i||^2-\left|\left|\sum_{i=1}^ku_i\right|\right|^2\right).$$

Examining $(1)$ at each coordinate, we conclude that it suffices to show that

$$k\sum_{i=1}^ka_i^2\geq \left(\sum_{i=1}^ka_i\right)^2\ \ \ (2)$$

for any $k$-tuple $(a_1,\ldots,a_k)$ of real numbers. Indeed, then we will have that

$$k\sum_{i=1}^ku_{ij}^2\geq \left(\sum_{i=1}^ku_{ij}\right)^2,\ \forall j=1,\cdots,n$$

and $(1)$ will follow by summing over all $j$'s. Now, $(2)$ can be easily proved by applying C-S to the vectors $(1,\ldots,1)$ and $(a_1,\ldots,a_k).$ An other proof of $(2)$ can be obtained by applying Jensen's inequality to the convex function $x\mapsto x^2.$