This is B.17 from Fundamentals of Convex Analysis by Jean-Baptiste Hiriart-Urruty, Claude Lemaréchal.
Let $f: S^{++}(\mathbb{R}^n) \to \mathbb{R}$ be $$f(M) := \frac{-1}{tr(M^{-1})}$$ Then show $f$ is convex.
Is the trace of inverse matrix convex? shows that $tr(M^{-1})$ is convex in $M$, but this doesn't help with this problem.
If we let $A\in S^{++}(\mathbb{R}^n), B \in S(\mathbb{R}^n)$, and let $\phi(t) = f(A + tB)$, then try to show $\phi''(0) \ge 0$, we get $$tr(A^{-1})tr(A^{-1}BA^{-1}BA^{-1})\ge tr(A^{-1}BA^{-1})^2$$ Then let $C = BA^{-1}$, and change to an eigenvector basis for $A^{-1}$, so that $A^{-1} = diag(\lambda_1, \cdots, \lambda_n), C = [c_{ij}]$, then it becomes $$\sum_{ij}\lambda_i \lambda_j \sum_k c_{jk}^2 \ge \sum_{ij}\lambda_i \lambda_j c_{ii}c_{jj}$$ But I don't know how to prove this inequality.