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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):

.. code-block:: bash

   pip install apache-tvm

Verify:

.. code-block:: bash

   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:

.. code-block:: bash

   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):

.. list-table::
   :header-rows: 1
   :widths: 18 24 58

   * - 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
