The state-of-the-art in high-performance deep learning today is primarily driven by manually developed libraries optimized and highly tuned by expert programmers using low-level abstractions with significant effort. This effort is often repeated for similar hardware and future ones. In this work, we pursue and evaluate the more modular and reusable approach of using compiler IR infrastructure to generate libraries by encoding all the required optimizations as a sequence of transformations and customized passes on an IR. We believe that until the recent introduction of MLIR (Multi-level intermediate representation), it had been hard to represent and transform computation at various levels of abstraction within a single IR. Using the MLIR infrastructure, we build a transformation and lowering pipeline to automatically generate near-peak performance code for matrix-matrix multiplication (matmul) as well as matmul fused with simple pointwise operators targeting tensor cores on NVIDIA GPUs. On a set of problem sizes ranging from 256 to 16384, our performance evaluation shows that we can obtain performance that is 0.95× to 1.19× and 0.80× to 1.60× of cuBLAS for FP32 and FP16 accumulate respectively on NVIDIA’s Ampere based Geforce 3090 RTX. Furthermore, by allowing the fusion of common pointwise operations with matrix-matrix multiplication, we obtain performance ranging from 0.95× to 1.67× of a cuBLAS-based implementation. Additionally, we present matmul-like examples such as 3-d contraction and batched matmul, which the pipeline can efficiently handle while providing competitive performance. We believe that these results motivate further research and engineering on automatic domain-specific library generation using compiler IR infrastructure for similar specialized accelerators.