The actor model is present in several mission-critical systems, such as those supporting WhatsApp and Twitter. These systems serve thousands of clients simultaneously, therefore demanding substantial computing resources usually provided by multiprocessor and multicore platforms. Non-Uniform Memory Access (NUMA) architectures account for an important share of these platforms. Yet, little or no research has been done on the suitability of the current actor runtime environments for these machines. Current runtime environments assume a flat memory space, thus not performing as well as they could. The NUMA environment presents challenges to the actor model runtime environment in fields varying from memory management to scheduling and load-balancing. In this document we analyze and characterize actor based applications to, in light of the above, propose improvements to actor runtime environments. As a proof of concept, we have applied our ideas in a real actor runtime environment, the Erlang virtual machine. This modified virtual machine uses the NUMA characteristics and the application knowledge to take better memory management, scheduling and load-balancing decisions. We have evaluated this modified runtime environment using standard benchmarks and, taking the default virtual machine as a baseline, we improved the performance of the tested applications by a factor of 2.50 on the best case while limiting our slowdown on the worst case by a factor of 1.09.