Triton for MTIA: Bridging the Programming Model Gaps for Custom AI Accelerators

This paper presents the first production-scale application of Triton on a custom ML accelerator, Meta's MTIA-2i, via a new compiler backend, TorchInductor code generation enhancements, and minimal language extensions exposing MTIA-specific architectural features. Triton-MTIA kernels achieve performance competitive with expert-tuned C++ implementations, and manually written plus Inductor-generated Triton kernels were deployed in production across roughly 60 model types, covering 50% of layers and 47% of non-GEMM execution time for these models.