Open-Source Training Kernels for Minimax Sparse Attention Released
A researcher has released performant open-source training kernels for Minimax Sparse Attention, a technique used in several frontier AI models. The kernels, developed for Hopper and Blackwell GPUs, are expected to accelerate training times. The release aims to make sparse attention more accessible to the AI community. However, the implementation is not officially affiliated with Minimax.
Key points
- The open-source training kernels for Minimax Sparse Attention were developed by a researcher using CuTeDSL for Hopper and Blackwell GPUs.
- The kernels are expected to accelerate training times for AI models using sparse attention, a technique used in frontier models like GLM-5.2 and DSv4.
- The release aims to make sparse attention more accessible to the AI community, particularly for models that use Group-Wise Query Attention (GQA) instead of Masked Language Modeling (MLA).
- The implementation is not officially affiliated with Minimax, and the researcher notes that this is not an official implementation.
A researcher has released open-source training kernels for Minimax Sparse Attention (MSA), a technique used in several frontier AI models. The kernels, developed for Hopper and Blackwell GPUs using CuTeDSL, are expected to accelerate training times for AI models.
The release aims to make sparse attention more accessible to the AI community, particularly for models that use Group-Wise Query Attention (GQA) instead of Masked Language Modeling (MLA). Currently, no western labs have adopted MLA into their training, making the prevailing sparse attention formulation in frontier models like GLM-5.2 and DSv4 inaccessible to models here.
The implementation is not officially affiliated with Minimax, and the researcher notes that this is not an official implementation. The release is expected to be a significant development for the AI community, particularly for researchers working on sparse attention and its applications.
Sources
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