There is no single Vulkan out-of-memory fix for on-device diffusion: first capture the exact error, Vulkan operation, and inference stage, then determine whether the failure is in host memory, device memory, mapping, or the runtime’s own budget. On phones, CPU and GPU often draw on shared system memory, so a displayed GPU-memory figure alone may not explain the failure.
Start by identifying exactly where the failure occurs
“Out of memory” describes several different failures, and the right response depends on which one occurred. Before changing settings, preserve the complete error text and the runtime or validation logs. Record:
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- Device make and model, SoC and GPU, operating system, GPU driver, and Vulkan version and extensions.
- Inference application and version, model or checkpoint, and precision.
- Image dimensions, batch size, and any other workload settings the application exposes.
- The first failing stage: model loading, buffer or image allocation, memory mapping, inference, or output decoding.
- The failing Vulkan API operation, exact
VkResult, requested allocation size, and memory type or heap, if the logs expose them.
The first failing operation matters: a model-load allocation error and an inference-time allocation error may call for different remedies. Do not infer an application setting or command-line switch from the Vulkan error alone.
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VK_ERROR_OUT_OF_DEVICE_MEMORY
This return code indicates that the implementation could not satisfy a device-memory allocation. It does not, by itself, prove that all physical memory is used. The Vulkan specification allows implementation-dependent limits on the size of a single allocation, and allocation-count or per-heap constraints can also matter. Total memory that appears available is not proof that a particular request can succeed.
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VK_ERROR_OUT_OF_HOST_MEMORY
This is distinct from a device-memory error: the failed allocation is for host memory. Check the operation and allocation details in the log rather than treating every Vulkan OOM as a shortage of GPU memory.
A memory-map failure
Mapping can fail for a different reason: the implementation may be unable to obtain the required contiguous virtual address range. That is not necessarily evidence that a device heap has been exhausted. Use the reported result and API call to classify the failure before choosing a mitigation.
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Account for shared memory on Android and other mobile devices
Mobile devices commonly use unified memory architecture (UMA): CPU and GPU workloads share system memory rather than using separate physical CPU and GPU heaps. Android guidance notes that VK_MEMORY_PROPERTY_DEVICE_LOCAL_BIT is less meaningful as an indicator of a separate physical pool on such devices than it is on a discrete GPU.
That means pressure can come from more than the diffusion model’s GPU allocations. CPU-side weights, activations, application state, the operating system, and other processes can all contribute to pressure on shared memory. Inspect whole-system memory use and concurrent workloads where the device exposes that information; do not rely only on an app’s “GPU memory” display or assume that desktop dedicated-VRAM behavior applies.
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Check whether the inference runtime imposes its own budget
A backend can make allocation and placement decisions beyond Vulkan’s general rules. For example, the stable-diffusion.cpp project documentation describes reserving 512 MiB of currently free device memory for scratch buffers and pipelines, and prioritizing components in diffusion, text-encoder, then VAE order. Those are documented behaviors of that project’s backend, not Vulkan requirements or a universal memory budget; implementation details may change.
If you use stable-diffusion.cpp, check the current project documentation and logs to see whether its memory-budget policy or component choices explain the failure. For another application, use its own documentation and diagnostics. The Vulkan error alone does not establish which model component a particular app loads first or what memory reserve it applies.
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Choose a mitigation that matches the failing stage
When peak device residency is the problem
A runtime designed for it can keep model weights in system RAM and stream them to the GPU when needed, reducing how much model data must remain resident in device memory. This can increase transfers and execution cost, and on a UMA device system RAM is not a separate, unlimited pool: CPU and GPU still share it. Confirm that the specific runtime supports this placement strategy before expecting a setting for it.
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A runtime or execution graph can plan tensor lifetimes and reuse or alias buffers when the values that occupy them are no longer live. This can lower peak memory demand, but it depends on graph and runtime support; it is not a Vulkan switch that every application exposes.
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When the workload is too large for the available budget
If the failure occurs during inference rather than loading, consult the application’s supported controls for workload size, such as image dimensions, batch, or precision. Changing a supported setting may reduce demand, but the available evidence does not establish a particular control, value, or guaranteed fix for any named consumer app. Change one variable at a time and keep the same failure record so you can tell whether the failing stage or allocation changed.
Compare the trade-offs before changing runtime design
| Approach | Potential memory effect | Other cost or constraint | What to verify |
|---|---|---|---|
| Keep weights in system RAM and stream them to the GPU | Can reduce peak device residency | Requires transfers; may add execution cost. On UMA, system memory is shared rather than a separate reserve. | The runtime supports this placement and streaming behavior. |
| Reuse buffers based on tensor lifetimes | Can reduce peak tensor-buffer demand when lifetimes do not overlap | Requires graph or runtime support and correct lifetime planning. | The implementation actually aliases or reuses eligible buffers. |
| Reduce workload using app-supported controls | May reduce demand, depending on the workload and implementation | Can change output size, quality, or runtime; a setting may not exist in the app. | The app documents the control and the failure changes under a controlled test. |
Interpret device-specific limits and benchmarks carefully
The Mali intermediate-geometry figure is not a diffusion limit
The Khronos Vulkan Documentation Project describes a Mali rendering case in which excessive intermediate geometry output can lead to VK_ERROR_DEVICE_LOST. Its documentation gives 180 MB for the intermediate geometry region on current Mali GPUs in that rendering context and says very high vertex load is the common case. That figure is not a diffusion-model memory target, phone-RAM figure, or general Vulkan heap cap.
Mobile diffusion papers are setup-specific
“Speed Is All You Need” by Zhou et al. (2023) and “Squeezing Large-Scale Diffusion Models for Mobile” (2023) describe mobile diffusion work under their own study setups. Their results provide context for mobile memory and compute constraints, not a guaranteed experience or compatibility claim for a current device or runtime. A benchmark is useful as a comparison only when the device, model, resolution, precision, step count, and runtime are sufficiently comparable.
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Use the error record to decide what to investigate next
- Capture the failure: save the exact result code, failing Vulkan call, allocation details, and logs.
- Locate the stage: establish whether the failure happens during load, allocation, mapping, inference, or output decoding.
- Check the right memory pool and pressure: distinguish host from device allocation; on UMA, consider whole-system memory and competing workloads.
- Inspect backend policy: check the inference runtime’s current memory budget, placement behavior, and component-loading decisions.
- Test only supported mitigations: use documented application controls or runtime capabilities, changing one variable at a time.
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