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

Meta researchers reported the first production-scale application of Triton on a custom ML accelerator, MTIA-2i, in an arXiv paper submitted 31 July 2026 and revised 12 August 2026. The team built a new compiler backend targeting MTIA-2i, enhanced TorchInductor code generation, and proposed minimal language extensions for MTIA-specific architectural features, with Triton-MTIA kernels achieving performance competitive with expert-tuned C++ implementations. Manually written and Inductor-generated Triton kernels were deployed in production across approximately 60 model types, covering 50% of layers and 47% of non-GEMM execution time for those models.

Computer Science Programming Languages Submitted on 31 Jul 2026 v1 https://arxiv.org/abs/2608.00325v1 , last revised 12 Aug 2026 this version, v2 Title:Triton for MTIA: Bridging the Programming Model Gaps for Custom AI Accelerators View PDF https://arxiv.org/pdf/2608.00325 HTML experimental https://arxiv.org/html/2608.00325v2 Abstract:The rapid growth in machine learning workloads has fueled the proliferation of custom accelerator architectures. Designed from the ground up, these accelerators often expose programming models that are distinct from GPUs. While hyperscalers and AI chip startups continue to innovate in this space, achieving broad operator coverage to support diverse models remains a major challenge. Additionally, an easy-to-use, high-level kernel programming language is important for rapid iteration of models and kernels. Triton, together with TorchInductor, addresses these issues on GPUs, but its viability on accelerators with different programming models has yet to be established. In this work, we present the first production-scale application of Triton on a custom ML accelerator, MTIA-2i, developed by Meta. To support MTIA-2i, we develop a new compiler backend that targets it, introduce enhancements to TorchInductor code generation, and propose minimal language extensions that expose MTIA-specific architectural features. We demonstrate that Triton-MTIA kernels achieve performance competitive with expert-tuned C++ implementations. Leveraging these development efficiency gains, we successfully deployed manually written and Inductor-generated Triton kernels in production across approximately 60 different model types, accounting for 50% of layers and 47% of non-GEMM execution time for these models. Our results provide compelling evidence that DSLs like Triton can bridge the programming model gaps between ML frameworks, kernels, and custom accelerators, enabling rapid innovation and efficient deployment at scale. Submission history From: Haishan Zhu view email https://arxiv.org/show-email/82bbc001/2608.00325 Fri, 31 Jul 2026 22:26:39 UTC 203 KB \ v1\ https://arxiv.org/abs/2608.00325v1 v2 Wed, 12 Aug 2026 21:20:35 UTC 203 KB Additional Features References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .