A set of high performance kernels and communication libraries now allows developers to train models on Huawei Ascend hardware using a stack previously optimized for Nvidia. DeepSeek open-sourced this infrastructure—comprising TileLang, DeepGEMM, DeepEP, TileKernels, FlashMLA, and DeepSelect—to enabledomestic AI development in China. In official tests for Dense GEMM, the DeepGEMM Ascend library reached 99.8% of the theoretical hardware limit and 98% on MegaMoE.

The release was developed in collaboration with Huawei for the Ascend 950 128 card supernode, with deep optimizations for computation and communication. TileLang, a centerpiece of the suite, is a high-level language that permits writing operators in a Python-like format that compiles for Nvidia, AMD, and Ascend hardware. DeepSeek utilized TileLang to implement a large number of operators during the training of its V4 model series.