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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

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
webgpu-spirv Experimental
GPU support on the Web via WGSL for WebGPU

Tip - listing available backends

The list of compiler target backends can be queried:

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

Registered target backends:


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.