The computing landscape has shifted from homogeneous x86 architectures to a heterogeneous mix including ARM and RISC-V, introducing challenges for high performance computing. Simultaneously, numerical formats have diversified, with modern deep learning workloads favoring low- and mixed-precision arithmetic to boost efficiency. This complexity demands advanced compiler frameworks and portable performance tools. This work explores using the Exo framework to automatically generate optimized, mixed-integer precision gemm micro-kernels for ARM-NEON and RISC-V Vector, easing the burden of architecture-aware tuning for developers of transformer pipelines. Our experimental evaluation on state-of-the-art, low-power CPUs shows competitive performance for mixed-precision integer arithmetic using a representative quantized BERT model. The benefits can be expected to carry over to convolutional neural networks.