tilelang
Domain-specific language designed to streamline the development of high-performance GPU/CPU/Accelerators kernels
Tile Language lets you write fast GPU and accelerator code for AI math operations using simple Python instead of low-level hardware code. You describe the math, and the compiler makes it run fast on your chip.
Modern AI models rely heavily on math operations like matrix multiplication and attention mechanisms that run on specialized chips like GPUs. Writing these operations to be fast is incredibly difficult, usually requiring deep expertise in low-level hardware programming. Tile Language (tile-lang) solves this by letting developers write high-performance GPU and accelerator kernels using simple Python syntax. You describe what the math should do, and the compiler handles the hard work of making it run fast on the hardware.
Under the hood, the project sits on top of a compiler framework called TVM. When you write a kernel in tile-lang, you can specify things like how data should be chunked or copied in parallel, and the tool translates that into optimized code for your specific chip. It supports a wide range of hardware, including NVIDIA and AMD GPUs, Apple Metal devices, and even Huawei Ascend chips. The project claims performance on par with hand-optimized assembly code, achieving top-tier speed for complex operations like FlashAttention using as few as 80 lines of Python.
This tool is primarily for engineers and researchers building the foundational layers of AI systems. For example, if you are building a custom attention mechanism for a new large language model and need it to run quickly on NVIDIA H100s or AMD MI300Xs, you would use this instead of writing raw hardware code. It is already used in real-world projects like Microsoft's BitBLAS and AttentionEngine, which are libraries that help AI models run efficiently.
What makes this project notable is the tradeoff it offers: you get the productivity and readability of Python without sacrificing the extreme performance usually required for AI workloads. It provides fine-grained control when you need it—like specifying memory layout for better cache usage—but abstracts away the tedious, hardware-specific details that make low-level programming so slow and error-prone.
Where it fits
- Write a custom attention mechanism for a new large language model that runs fast on NVIDIA H100 GPUs.
- Build optimized matrix multiplication kernels for AI workloads without writing low-level hardware code.
- Create performance-critical AI operations that run across multiple hardware platforms like AMD and Apple Metal.