This paper explores the roles of acute infection and concurrent partnerships in HIV transmission dynamics among young adults in Zimbabwe using realistic representations of the partnership network and all published estimates of stage-specific infectivity. We use dynamic exponential random graph models to estimate partnership network parameters from an empirical study of sexual behavior and drive a stochastic simulation of HIV transmission through this dynamic network. Our simulated networks match observed frequencies and durations of short- and long-term partnerships, with concurrency patterns specific to gender and partnership type. Our findings suggest that, at current behavior levels, the epidemic cannot be sustained in this population without both concurrency and acute infection; removing either brings transmission below the threshold for persistence. With both present, we estimate 20-25% of transmissions stem from acute-stage infections, 30-50% from chronic-stage, and 30-45% from AIDS-stage. The impact of acute infection is strongly moderated by concurrency. Reducing this impact by reducing concurrency could potentially end the current HIV epidemic in Zimbabwe.