Installation

There are two pieces:

• the TIRx compiler (tvm.tirx), which ships inside Apache TVM — this is all you need to write and compile kernels;

• the optional kernel library (tirx-kernels), a set of ready-made GEMM and attention kernels built with TIRx.

Requirements

• Python ≥ 3.10.

• An NVIDIA GPU with a recent CUDA toolkit. The bundled kernels target Blackwell (sm_100a); the compiler itself targets GPUs and accelerators more broadly.

Install the TIRx compiler

Install the Apache TVM wheel (the TIRx compiler is the tvm.tirx module):

pip install apache-tvm

Verify:

python -c "import tvm, tvm.tirx; print(tvm.__version__)"

Install the kernel library (optional)

tirx-kernels provides prebuilt kernels (fp16_bf16_gemm, fp8_blockwise_gemm, nvfp4_gemm, flash_attention4). It has no PyPI wheel — install it from source:

git clone https://github.com/mlc-ai/tirx-kernels
cd tirx-kernels
pip install -e .

Its runtime dependencies are not pulled from PyPI and must be available separately (they are imported lazily, so import tirx_kernels and kernel discovery work without them — they are only needed to actually compile/run a kernel):

Dependency	Needed by	Notes
tvm.tirx	all kernels	the TIRx compiler (installed above, or put a source checkout’s python/ on PYTHONPATH)
torch	all kernels	a CUDA build matching your GPU
deep_gemm	fp8_blockwise_gemm	optional — quantization helpers and the reference baseline
flashinfer	nvfp4_gemm	optional — quantization and the baseline