Deployment configurations

IREE provides a flexible set of tools for various deployment scenarios. Fully featured environments can use IREE to load programs on demand and to take advantage of multi-threaded hardware, while embedded systems can bypass IREE's runtime entirely or interface with custom accelerators.

Stable configurations

CPU for general purpose CPU deployment
CPU - Bare-Metal with minimal platform dependencies
GPU - Vulkan
GPU - CUDA/ROCm

These are just the most stable configurations IREE supports. Feel free to reach out on any of IREE's communication channels if you have questions about a specific platform, hardware accelerator, or set of system features.

Compiler target backends

Compiler target backends are used to generate executable code for hardware APIs and device architectures. Compiler targets may implement special optimizations or generate distinct code for certain device/architecture/performance profiles.

When compiling programs, a list of target backends must be specified via

--iree-hal-target-backends= (command-line)
target_backends=[...] (Python)

Target backend	Description	Compatible HAL devices
`llvm-cpu`	Code generation for CPU-like devices supported by LLVM	`local-sync`, `local-task`
`vmvx`	Portable interpreter powered by a microkernel library	`local-sync`, `local-task`
`vulkan` or `vulkan-spirv`	Portable GPU support via SPIR-V for Vulkan	`vulkan`
`cuda`	NVIDIA GPU support via PTX for CUDA	`cuda`
`rocm`	Experimental AMD GPU support via HSACO for ROCm	`rocm`
`webgpu-wgsl`	Experimental GPU support on the Web via WGSL for WebGPU	`webgpu`
`metal` or `metal-spirv`	Experimental GPU support on Apple platforms via MSL for Metal	`metal`

!!! tip “Tip - listing available backends” The list of compiler target backends can be queried:

=== "Command-line"

    ```console
    $ iree-compile --iree-hal-list-target-backends

    Registered target backends:
        llvm-cpu
        metal
        metal-spirv
        vmvx
        vmvx-inline
        vulkan
        vulkan-spirv
    ```

=== "Python bindings"

    ```python
    iree.compiler.query_available_targets()

    ['cuda',
     'llvm-cpu',
     'metal',
     'metal-spirv',
     'vmvx',
     'vmvx-inline',
     'vulkan',
     'vulkan-spirv']
    ```

Runtime HAL drivers/devices

Runtime HAL devices call into hardware APIs to load and run executable code. Devices may use multithreading or other system resources, depending on their focus and the build configuration.

HAL device	Description
`local-sync`	Synchronous local CPU device with inline execution
`local-task`	Multithreaded local CPU device using a ‘task’ executor
`vulkan`	Portable GPU execution using the Vulkan API
`cuda`	NVIDIA GPU execution using CUDA
`rocm`	Experimental AMD GPU execution using ROCm
`webgpu`	Experimental GPU execution on the web using WebGPU
`metal`	Experimental GPU execution on Apple platforms using Metal

Additional HAL drivers can also be defined external to the core project via IREE_EXTERNAL_HAL_DRIVERS.