Deployment configurationslink

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 configurationslink

CPU for general purpose CPU deployment
CPU - Bare-Metal with minimal platform dependencies
GPU - Vulkan for cross-platform usage and interop with graphics applications
GPU - CUDA for NVIDIA-specific solutions
GPU - ROCm for AMD-specific solutions
GPU - Metal for running on Apple hardware

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 backendslink

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-spirv`	Portable GPU support via SPIR-V for Vulkan	`vulkan`
`cuda`	NVIDIA GPU support via PTX for CUDA	`cuda`
`metal-spirv`	GPU support on Apple platforms via MSL for Metal	`metal`
`rocm`	Experimental AMD GPU support via HSACO for ROCm	`rocm`
`webgpu-spirv`	Experimental GPU support on the Web via WGSL for WebGPU	`webgpu`

Tip - listing available backends

The list of compiler target backends can be queried:

Command-linePython bindings

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

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

iree.compiler.query_available_targets()

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

Runtime HAL drivers/deviceslink

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
`metal`	GPU execution on Apple platforms using Metal
`rocm`	Experimental AMD GPU execution using ROCm
`webgpu`	Experimental GPU execution on the web using WebGPU

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