There is no single Vulkan “out-of-memory” fix for on-device diffusion. First capture the exact error, the Vulkan operation that failed, and the stage of inference; then determine whether the limit is device memory, host or shared system memory, mapping, or a runtime’s own budget. Only then try a mitigation supported by your inference runtime.
What to record before changing settings
Keep the original error text and logs. A useful failure record lets you distinguish an allocation limit from a model-loading problem, mapping failure, or runtime capacity check—and makes a fix reproducible.
- Device make and model, SoC and GPU, operating-system version, GPU driver, Vulkan version, and relevant extensions.
- Inference application and version; model or checkpoint; precision; image dimensions; and batch size.
- The first failing stage: model load, buffer or image allocation, memory mapping, inference, or output decoding.
- The exact Vulkan result and API operation, plus the requested allocation size and memory type or heap when the runtime exposes them.
- Validation-layer and runtime logs, including any earlier warnings that precede the reported failure.
Do not start by changing several workload settings at once. Without the failing stage and exact result, a successful retry may hide the cause rather than identify it.
Which kind of Vulkan memory failure is it?
“Out of memory” can describe different failures. Vulkan distinguishes host-memory and device-memory allocation errors, and a mapping operation can fail for a reason other than exhausted physical heap capacity. The Vulkan specification also allows implementation-dependent limits on an individual allocation and imposes allocation-count and per-heap constraints. Consequently, a device can appear to have free memory in aggregate while a particular request still fails.
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| Symptom or result | What it points to | What to check |
|---|---|---|
VK_ERROR_OUT_OF_DEVICE_MEMORY |
A device-memory allocation failed. This does not by itself show whether the cause was overall pressure, a limit on one allocation, or another implementation constraint. | Record the request size, memory type or heap, operation, and whether it happened at load time or during inference. |
VK_ERROR_OUT_OF_HOST_MEMORY |
A host-memory allocation failed; it is distinct from a device-memory allocation error. | Check system-memory pressure, model-loading behavior, and concurrent applications, along with the runtime logs. |
| Memory mapping failure | The implementation may have been unable to obtain the required contiguous virtual address range; this is not necessarily proof that a physical heap was full. | Record the mapping operation and exact result. Do not treat a mapping failure as interchangeable with either allocation error. |
VK_ERROR_DEVICE_LOST during a rendering workload |
Some platform-specific failures can surface as device loss rather than a direct out-of-memory result. | Look for a documented platform or workload-specific cause before applying a general memory fix. |
Why Android memory readings can mislead
On Android and other unified-memory designs, CPU and GPU commonly share physical system memory rather than drawing from separate pools of dedicated VRAM. Android notes that VK_MEMORY_PROPERTY_DEVICE_LOCAL_BIT is therefore less meaningful as an indicator of a separate physical pool than it is on a discrete-GPU desktop.
Account for the memory used by CPU-side model weights, activations, the application itself, and other processes—not just a number labeled “GPU memory.” Closing other workloads or retrying with less system pressure can be a useful diagnostic, but it will not fix an implementation-specific allocation limit or a runtime budget check.
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Find the stage and the runtime’s own budget
Separate a Vulkan resource-allocation failure from a mapping failure and from an application or backend deciding that its own capacity limit has been reached. The distinction matters: changing a model workload may help with a peak during inference, while it may do nothing for a single oversized allocation during model loading.
If you use stable-diffusion.cpp
The stable-diffusion.cpp backend documentation describes reserving 512 MiB of currently free device memory for scratch buffers and pipelines. It also describes prioritizing components in diffusion, text-encoder, then VAE order. These are project-specific budgeting and placement choices, not Vulkan requirements or a universal minimum-memory rule. Check the documentation for the version you are running, since implementation details can change.
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If the first failure is unclear
Use the first failing API operation in the runtime or validation logs, rather than the final user-facing “out of memory” message alone. If the application does not expose the allocation size, memory type, or operation, report that limitation when seeking help; do not infer a specific heap failure from a generic message.
Choose a mitigation that your runtime actually supports
Once you know where the peak occurs, compare possible approaches by device-memory residency, host or shared-memory demand, transfer overhead, and runtime support. Neither approach below is guaranteed to be exposed as a user-facing setting.
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| Approach | Potential benefit | Trade-off or requirement | Best fit to investigate |
|---|---|---|---|
| Keep model weights in system RAM and stream them to the GPU | Can reduce peak GPU residency when a model is too large to keep entirely in device memory. | Requires runtime support and can add transfers or execution cost; shared system memory is still needed for the weights. | A failure caused by device-memory residency, if the inference backend implements streaming. |
| Reuse tensor storage after values are no longer live | Graph planning can alias buffers across tensors whose live ranges do not overlap, reducing peak memory use. | Requires graph or runtime support and correct knowledge of tensor lifetimes; it is not necessarily a switch available in an app. | A peak during inference when intermediate tensors drive memory use and the backend can plan or reuse their storage. |
| Reduce the workload using settings supported by the app | A smaller workload may reduce resource demand in some configurations. | The relevant controls and their effects are application-specific; no particular resolution, batch, precision, or step setting is established as a universal fix. | A repeatable inference-time failure, when the app documents workload controls and you can change one variable at a time. |
Check the application’s own documentation before looking for flags or switches. Do not assume a command-line option or memory-saving feature exists simply because the underlying technique is possible in Vulkan.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep the Mali rendering example in its lane
Khronos documentation describes a Mali rendering scenario where excessive intermediate geometry output can lead to VK_ERROR_DEVICE_LOST. For current Mali GPUs, that documentation describes a 180 MB intermediate geometry region in that rendering context; very high vertex load is given as a common case. This is not a diffusion memory limit, a phone-RAM figure, or a general Vulkan heap cap. It is relevant only if the failing workload and error match the documented rendering scenario.
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Use mobile diffusion benchmarks cautiously
Published results show that mobile diffusion performance depends on the particular model, device, and test setup. “Speed Is All You Need” by Zhou et al. (2023) reports GPU-aware on-device diffusion optimizations, including a Samsung S23 Ultra case. “Squeezing Large-Scale Diffusion Models for Mobile” (2023) reports a mobile implementation and Android performance results. Neither paper establishes compatibility with a current app or a guaranteed result on another device.
Before comparing a paper with your run, match the device, model, resolution, precision, step count, and runtime. No general authoritative minimum RAM or VRAM requirement for running an on-device diffusion model is established here, so a benchmark should not be turned into a universal memory threshold.
Quick Recap
A practical triage sequence
- Preserve the failure. Save the exact result, operation, logs, and workload details before retrying.
- Classify it. Determine whether it is a device allocation error, host allocation error, mapping failure, device loss, or a backend-specific capacity decision.
- Locate the first failing stage. Distinguish loading, allocation, mapping, inference, and output decoding.
- Check system-wide pressure. On shared-memory devices, account for CPU and GPU use and concurrent workloads rather than relying only on a GPU-memory display.
- Check the backend’s documented policy. Confirm whether it reserves memory, streams weights, or supports tensor storage reuse in the version you run.
- Test one supported change at a time. Use documented app settings or runtime features, then compare the exact failing stage and result with the original record.
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