TileKernels
An open-source library of general vector computation and memory-access operators released by DeepSeek, serving as part of the low-level computation components for model training.
Core Facts
Timeline (last 90 days)
DeepEP corresponds to NCCL, handling communication between multiple GPUs; FlashMLA corresponds to FlashAttention, used for ultra-long text processing; TileKernels corresponds to CUTLASS; DeepSelect corresponds to data pipelines such as DALI.
The supporting resources developed around TileLang for Ascend include the TileLang main repository, the TileKernels operator library, an Ascend-oriented operator library, LearningHub development courses, and AI-assisted development tools.
The open-source list includes TileLang, DeepGEMM Ascend, DeepEP Ascend, TileKernels, FlashMLA, and DeepSelect, all hosted on GitHub under the MIT license.
The six components have distinct roles: DeepGEMM accelerates general matrix operations, DeepEP handles large-scale cross-device communication, TileKernels provides common vector computation and memory access operators, FlashMLA provides sparse attention operators, and DeepSelect performs efficient data selection.
On September 30, 2025, DeepSeek open-sourced a batch of underlying infrastructure components, including operators, a programming language, compute libraries, and communication libraries
All Facts (5)
On September 30, 2025, DeepSeek open-sourced a batch of underlying infrastructure components, including operators, a programming language, compute libraries, and communication libraries
65%UnverifiedDeepEP corresponds to NCCL, handling communication between multiple GPUs; FlashMLA corresponds to FlashAttention, used for ultra-long text processing; TileKernels corresponds to CUTLASS; DeepSelect corresponds to data pipelines such as DALI.
50%UnverifiedThe supporting resources developed around TileLang for Ascend include the TileLang main repository, the TileKernels operator library, an Ascend-oriented operator library, LearningHub development courses, and AI-assisted development tools.
50%UnverifiedThe six components have distinct roles: DeepGEMM accelerates general matrix operations, DeepEP handles large-scale cross-device communication, TileKernels provides common vector computation and memory access operators, FlashMLA provides sparse attention operators, and DeepSelect performs efficient data selection.
50%UnverifiedThe open-source list includes TileLang, DeepGEMM Ascend, DeepEP Ascend, TileKernels, FlashMLA, and DeepSelect, all hosted on GitHub under the MIT license.
50%