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What Vulkan does—and what it does not do
Android describes Vulkan as a low-overhead, cross-platform API for high-performance 3D graphics. It lets software manage GPU work through a defined interface and supports SPIR-V, a format for GPU programs. Vulkan is therefore relevant to apps that implement native GPU work, including graphics and compute tasks that may be part of an ML application.
Vulkan is not, by itself, a model format, an inference engine, or the layer that selects and runs an ML model’s operations. An ML runtime handles that work. A runtime may offer an acceleration delegate that can use specialized hardware, while the device’s platform and vendor software provide access to that hardware. The exact low-level backend can depend on the runtime and device.
Which Android ML stack to use now
LiteRT for custom on-device inference
Android’s current custom-ML documentation recommends LiteRT with Google Play services. Android calls LiteRT its official ML inference runtime and documents delegates distributed through Google Play services for acceleration on specialized hardware such as GPUs or NPUs. Its Acceleration Service API can help select an acceleration configuration at runtime.
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These options depend on runtime and device support, model compatibility, and delegate coverage. A GPU delegate being available does not mean every model operation will run on the GPU, nor that every target phone will use a GPU for a given model. Most importantly, Android’s documentation establishes the presence of GPU delegates, but does not specify Vulkan as the universal low-level backend for LiteRT GPU inference.
NNAPI and migration
NNAPI was deprecated in Android 15. Deprecation does not mean it immediately stopped working, but Android’s NDK documentation recommends migrating performance-critical workloads to alternatives, giving the TensorFlow Lite GPU runtime as an example. The migration guidance describes TensorFlow Lite in Google Play services and an optional GPU delegate as migration options. For new custom-ML work, use the currently documented LiteRT path rather than treating NNAPI as Android’s preferred new interface.
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How Vulkan relates to GPU-accelerated inference
The useful mental model is a stack: an app calls an ML runtime; the runtime runs the model and may select an acceleration delegate; and the delegate works with device software to use available hardware. Vulkan is one Android GPU interface relevant to native GPU and graphics/compute implementations. It is not interchangeable with LiteRT, and the fact that a device supports Vulkan does not prove a particular ML runtime or delegate uses it.
Vulkan’s low-overhead design and control over GPU work can be useful when developers build GPU functionality themselves or use software that targets Vulkan. But those general API characteristics do not establish a specific ML speedup, reduced battery use, or universal compatibility. Actual inference performance depends on the model’s operators, input sizes, precision, runtime and delegate coverage, GPU, and driver.
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Check support on your target devices
Android’s Vulkan overview says Vulkan is available starting with Android 7.0 (API level 24). It also says all 64-bit devices running Android 10.0 (API level 29) or higher support Vulkan 1.1. These platform statements help narrow compatibility, but they do not guarantee that a particular device’s driver will run your application or that a given ML delegate will use Vulkan.
The same Android overview reports that 85% of active Android devices support Vulkan, but the retrieved page statement does not give a measurement date. Treat it as Android’s undated availability statement, not as a 2026 measurement or a measure of ML acceleration.
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Android’s Vulkan Profiles page reports these profile support rates based on active Vulkan-supporting-device data from October 2025:
| Vulkan profile | Share of active Vulkan-supporting devices | What the figure means |
|---|---|---|
| AVP 2025 | 80.1% | Support for this profile’s feature set among active Vulkan-supporting devices, based on October 2025 data. |
| AVP 2022 | 86.5% | Support for this profile’s feature set among active Vulkan-supporting devices, based on October 2025 data. |
| AVP 2021 | 95.5% | Support for this profile’s feature set among active Vulkan-supporting devices, based on October 2025 data. |
These are profile feature-set coverage figures, not percentages of all Android devices and not measures of model speed. Validate the actual runtime, delegate, model, and drivers on representative devices. Android’s native-engine guidance recommends considering an OpenGL ES fallback for older devices whose Vulkan implementations may be unreliable; that is graphics compatibility advice, not a specified ML fallback mechanism.
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Evaluate the trade-offs for your app
Measure the workload, not the API label
Compare the complete inference path on representative target hardware. Measure latency and throughput for your real model and input sizes, check which operations the chosen delegate accelerates, and verify what happens when acceleration is unavailable or incomplete. The cited Android documentation does not provide a Vulkan-specific Android ML benchmark, so claims about speed need measurements from the workload and devices in question.
Account for on-device costs
Android lists lower network latency, offline availability, and keeping data on the device among the potential benefits of on-device inference. It also notes battery consumption and model storage as costs. These are general on-device ML considerations: they are not guarantees of privacy, speed, or battery savings from Vulkan itself.
- Runtime: Confirm the LiteRT version and delegate path supported by the app and device.
- Hardware and drivers: Check GPU or NPU availability and Vulkan capabilities where your implementation requires them.
- Model fit: Verify operator coverage, precision, and behavior when some work cannot use the selected delegate.
- Operational needs: Weigh offline use and data locality against battery use and model size.
- Compatibility: Test the oldest and most common devices you support, and plan a suitable fallback for your app’s graphics or inference path.
Bottom line for Android developers
Use LiteRT and its supported hardware delegates as the documented Android custom-ML path. Use Vulkan when your app or chosen implementation needs Vulkan’s GPU interface, but do not assume Vulkan support alone enables LiteRT GPU inference or that all LiteRT GPU delegates run on Vulkan. Check the target devices and drivers, and measure the actual model before promising a performance benefit.
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