The following concentration inequality for the supremum of a Gaussian process indexed by a separable metric space appears here: http://math.iisc.ac.in/~manju/GP/6-Concentration%20and%20comparison%20again.pdf
(this, to the best of my knowledge, is one of several lecture notes prepared by Prof. Manjunath Krishnapur, IISC Bangalore), page-22 (or page 1 in the pdf), exercise-2. 

Let $X$ be a centered, continuous Gaussian process on a separable metric space $T$ and suppose that $X^* := \sup_{t \in T} X_t$ is finite with probability $1$. Then, show that: $$\lim_{x\rightarrow +\infty}\frac{1}{x^2} \log \mathbb{P}\left(X^* \geq x\right) = -\frac{1}{2\sigma_T^2}~,$$
where $\sigma_T^2 := \sup_{t \in T} \mathbb{E} (X_t^2)$.

I need to use this result in one of my research works, so I need a proper reference, where this, or anything similar is proved. I basically want a reference, where an exponential concentration of the supremum of a continuous Gaussian process in a separable metric space (for me, Euclidean spaces suffice) in terms of its maximum variance, is proved. Can anyone help me? Thanks in advance.