title: Low-VRAM Inference: Measured Runtime Contract description: "Low-VRAM inference is a measured runtime configuration, not a hardware-tier promise; pin the model and backend, select only documented quantization, offload, or tiling paths, and record peak memory, latency, output fidelity, and failure behavior on the actual device." category: techniques tags: [memory, offloading, quantization, tiling, onnx, inference, evaluation] aliases: ["Memory-Efficient Inference", "GPU Memory Optimization"]
Low-VRAM Inference: Measured Runtime Contract¶
Low-VRAM support is not a table of GPU names or an advertised memory number. It is a versioned runtime configuration that must fit the actual model, resolution, batch, device, driver, backend, and concurrent workload while preserving the intended output contract.
Establish a baseline¶
Measure the unoptimized supported path on the target device first. Record:
- model/checkpoint digest, runtime/back-end/provider versions, device/driver, dtype, input shape, batch, and concurrency;
- peak allocated/reserved device memory, host memory, wall time, first-run behavior, and retry/failure output;
- output hashes or a task-specific fidelity/review result; and
- the device-memory budget that remains for activations, decoding, UI, and other process owners.
Do not infer a usable memory budget from nominal VRAM. Activation memory and runtime workspace can dominate model-weight size, and a device that starts a job can still fail part way through it.
Choose documented strategies¶
Diffusers memory documentation describes device placement and model, sequential, and group CPU offloading. Those operations have different memory/latency trade-offs and can install stateful hooks. Their compatibility depends on the pipeline and version.
| Strategy | Valid only when | Required measurement |
|---|---|---|
| Quantization | the named checkpoint/runtime supports the exact format | output fidelity, peak memory, load failures, and throughput |
| Model or sequential offload | the pipeline documents the offload path and call order | peak device/host memory and repeated-call latency |
| Group/device placement | the model and runtime support the selected mapping | component order, resets, and out-of-memory behavior |
| Spatial/latent tiling | the model's encoder/decoder or inference path supports it | seams, padding/crop behavior, protected-detail preservation |
| ONNX I/O binding | tensors are placed on the execution provider's target device | provider compatibility, data-copy behavior, and dynamic-shape handling |
ONNX Runtime I/O binding can avoid unnecessary transfers when tensors already reside on the target device. It is not a generic speedup: provider, tensor ownership, shapes, and memory lifetime must be validated together.
Failure policy¶
Select one explicit supported path per measured device profile. If the baseline does not fit, choose a documented alternative and re-measure. If no supported configuration fits, return a visible capability/error state or an explicitly approved CPU/remote path; do not silently swap model, precision, provider, or output quality.
Cache and compiled-engine behavior also belong to the contract. Bind caches to the model, runtime, device, and driver assumptions they require, and measure cold and warm runs separately.
Acceptance¶
A low-memory configuration is releasable only if it:
- stays within the measured memory budget over repeated runs;
- returns the intended output without unreviewed tiling or quantization artifacts;
- reports an actionable failure when inputs exceed its validated shape or memory envelope; and
- records the configuration used for every result.
This turns a memory optimization into an observable product capability rather than an untestable hardware promise.