The validation and deployment of novel research ideas in the field of Deep Learning is often limited by the availability of efficient compute kernels for certain basic primitives. In particular, operations that cannot leverage existing vendor libraries (e.g., cuBLAS, cuDNN) are at risk of facing poor device utilization unless custom implementations are written by experts – usually at the expense of portability. For this reason, the development of new programming abstractions for specifying custom Deep Learning workloads at a minimal performance cost has become crucial. We present Triton, a language and compiler centered around the concept of tile, i.e., statically shaped multi-dimensional sub-arrays. Our approach revolves around (1) a C-based language and an LLVM-based intermediate representation (IR) for expressing tensor programs in terms of operations on parametric tile variables and (2) a set of novel tile-level optimization passes for compiling these programs into efficient GPU code. We demonstrate how Triton can be used to build portable implementations of matrix multiplication and convolution kernels on par with hand-tuned vendor libraries (cuBLAS / cuDNN), or for efficiently implementing recent research ideas such as shift convolutions.