Have you ever wanted to multiply an $n \times d$ matrix $X$, with $n \gg d$, on the left by an $m \times n$ matrix $\tilde G$ of i.i.d. Gaussian random variables, but could not afford to do it because it was too slow? In this work we propose a new randomized $m \times n$ matrix $T$, for which one can compute $T \cdot X$ in only $O(\text{nnz}(X)) + \tilde O(m^2 \cdot d^{3})$ time, for which the total variation distance between the distributions $T \cdot X$ and $\tilde G \cdot X$ is as small as desired, i.e., less than any positive constant. Here $\text{nnz}(X)$ denotes the number of non-zero entries of $X$. Assuming $\text{nnz}(X) \gg m^2 \cdot d^{3}$, this is a significant savings over the naïve $O(\text{nnz}(X) m)$ time to compute $\tilde G \cdot X$. Moreover, since the total variation distance is small, we can provably use $T \cdot X$ in place of $\tilde G \cdot X$ in any application and have the same guarantees as if we were using $\tilde G \cdot X$, up to a small positive constant in error probability. We apply this transform to nonnegative matrix factorization (NMF) and support vector machines (SVM).