A half century ago, Huber evaluated the minimax asymptotic variance in scalar location estimation, $ \min_ψ\max_{F \in {\cal F}_ε} V(ψ, F) = \frac{1}{I(F_ε^*)} $, where $V(ψ,F)$ denotes the asymptotic variance of the $(M)$-estimator for location with score function $ψ$, and $I(F_ε^*)$ is the minimal Fisher information $ \min_{{\cal F}_ε} I(F)$ over the class of $ε$-Contaminated Normal distributions. We consider the linear regression model $Y = Xθ_0 + W$, $W_i\sim_{\text{i.i.d.}}F$, and iid Normal predictors $X_{i,j}$, working in the high-dimensional-limit asymptotic where the number $n$ of observations and $p$ of variables both grow large, while $n/p \rightarrow m \in (1,\infty)$; hence $m$ plays the role of `asymptotic number of observations per parameter estimated'. Let $V_m(ψ,F)$ denote the per-coordinate asymptotic variance of the $(M)$-estimator of regression in the $n/p \rightarrow m$ regime. Then $V_m \neq V$; however $V_m \rightarrow V$ as $m \rightarrow \infty$. In this paper we evaluate the minimax asymptotic variance of the Huber $(M)$-estimate. The statistician minimizes over the family $(ψ_λ)_{λ> 0}$ of all tunings of Huber $(M)$-estimates of regression, and Nature maximizes over gross-error contaminations $F \in {\cal F}_ε$. Suppose that $I(F_ε^*) \cdot m > 1$. Then $ \min_λ\max_{F \in {\cal F}_ε} V_m(ψ_λ, F) = \frac{1}{I(F_ε^*) - 1/m} $. Strikingly, if $I(F_ε^*) \cdot m \leq 1$, then the minimax asymptotic variance is $+\infty$. The breakdown point is where the Fisher information per parameter equals unity.