Matrix multiplication (GEMM) sits at the heart of scientific computing, data analytics, and modern AI workloads. While much attention is given to peak throughput and ideal matrix sizes, real-world performance often hinges on the “edges” i.e., non-ideal dimensions, cache boundaries, and vector tail cases that quietly dominate execution time. In this paper, we present a practical case study of optimizing GEMM in OpenBLAS for RISC-V vector architectures. We show how careful handling of edge conditions, cache reuse, and vectorization strategy can deliver measurable performance gains. Techniques include maximizing cache and register reuse with single-pass data traversal, swapping operands and deferring transposition for easier storage, combining full- and half-vector operations with scalar instructions to efficiently handle irregular dimensions, and leveraging strided segmented load/store vector intrinsics to sustain throughput even in non-ideal layouts. These optimizations are not just academic; small inefficiencies in GEMM propagate directly into AI inference latency and energy. By focusing on edge cases and architectural nuance, we can unlock meaningful improvements for real-world workloads. These optimizations give substantial gains; for example, a 6 x 3072 × 3072 SGEMM MatMul efficiency improves from 23.5% to 68.7% of the peak.