Despite the ability of modern processors to execute a variety of algorithms efficiently through instructions based on registers with ever-increasing widths, some applications present poor performance due to the limited interconnection bandwidth between main memory and processing units. Near-data processing has started to gain acceptance as an accelerator device due to the technology constraints and high costs associated with data transfer. However, previous approaches to near-data computing do not provide general-purpose processing, or require large amounts of logic and do not fully use the potential of the DRAM devices. These issues limited its wide adoption. In this paper, we present the Memory Vector Extensions (MVX), which implement vector instructions directly inside the DRAM devices, therefore avoiding data movement between memory and processing units, while requiring a lower amount of logic than previous approaches. MVX is able to obtain up to 211× increase in performance for application kernels with a high spatial locality and a low temporal locality. Comparing to an embedded processor with 8 cores and 2 memory channels that supports AVX-512 instructions, MVX performs 24× faster on average for three well known algorithms.