TileLang brings CUDA performance to Python

TileLang marks a significant shift toward Python-first GPU optimization, proving that accessibility does not have to come at the cost of hardware-level performance. It has been proven at scale by DeepSeek to power critical LLM components like Multi-Head Latent Attention and Mixture-of-Experts routing. With native support for NVIDIA Blackwell and FP4 quantization, it reduces development overhead by allowing complex kernels to be implemented in roughly 80 lines of Python instead of hundreds of lines of CUDA C++.