Let $A$ be a square random matrix of size $n$, with mean zero, independent but not identically distributed entries, with variance profile $S$. When entries are i.i.d. with unit variance, the spectral radius of $n^{-1/2}A$ converges to $1$ whereas the operator norm converges to 2. Motivated by recent interest in inhomogeneous random matrices, in particular non-Hermitian random band matrices, we formulate general upper bounds for $ρ(A)$, the spectral radius of $A$, in terms of the variance $S$. We prove (1) after suitable normalization $ρ(A)$ is bounded by $1+ε$ up to the optimal sparsity $σ_*\gg (\log n)^{-1/2}$ where $σ_*$ is the largest standard deviation of an individual entry; (2) a small deviation inequality for $ρ(A)$ capturing fluctuation beyond the optimal scale $σ_*^{-1}$; (3) a large deviation inequality for $ρ(A)$ with Gaussian entries and doubly stochastic variance; and (4) boundedness of $ρ(A)$ in certain heavy-tailed regimes with only $2+ε$ finite moments and inhomogeneous variance profile $S$. The proof relies heavily on the trace moment method.