The RISC-V ecosystem is evolving toward AI-oriented computing, with matrix-oriented proposal directions such as AME, VME, and IME attracting increasing attention. In LLM inference, matrix multiplication constitutes one of the dominant computation patterns, and quantized matrix multiplication is widely adopted by many accelerators to improve efficiency. In this setting, the practical value of matrix-oriented proposals depends not only on the instruction capabilities they provide, but also on how effectively representative operators can be mapped onto realistic execution flows. This work presents an operator-level profiling study of a currently discussed AME proposal for RISC-V AI. We first design representative matrix operators for quantized LLM-style workloads, then develop a gem5-based platform with support for the AME proposal, and profile matrix multiplication on this platform. Based on these observations, we further analyze scaled matrix multiplication as an extended operator flow and discuss a possible scaled matrix multiplication instruction strategy as a future optimization direction.