TileKernels GitHub repository (DeepSeek)

README, MIT. 'a library of dozens of highly optimized kernels implemented in TileLang ... provides kernels for several common operations in LLM training and inference, including mixture-of-experts routing, Engram, quantization, and manifold hyper-connections'. Feature list includes MoE top-k selection and scoring, per-token/per-block/per-channel FP8 and FP4 cast and dequantization with fused SwiGLU+quantization, RoPE. Requirements: Python 3.12+, PyTorch 2.13+, TileLang 0.1.15+; NVIDIA SM90 or SM100 with CUDA >= 13.1, or Ascend 950 with CANN >= 9.2.0, selected automatically at runtime behind the same Python APIs. Perf claim 'most kernels achieve performance close to the hardware's compute or memory bandwidth limits' is DeepSeek's own and carries no published numbers.