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Run image embeddings by preserving the model’s preprocessing contract, exporting or loading a compatible graph, selecting a runtime execution provider for the target device, and benchmarking the complete pipeline. A GPU is not automatically fastest, an NPU is not automatically most efficient, and a CPU is not automatically inadequate. The right choice depends on the model’s operators and shapes, the operating system and drivers, batch and concurrency requirements, precision, and the embedding quality your application needs.
What an image embedding is and why the device choice matters
An image embedding is a vector produced by a vision model. The vector represents features that can be used for image similarity, retrieval, clustering, recommendation, classification with a separate head, or multimodal matching. The output is useful only in the context of the model that produced it: changing the model, preprocessing, dimensionality, or numerical precision can change the meaning of the vectors.
Hugging Face describes image feature extraction as “the task of extracting semantically meaningful features given an image.” In CLIP, the image encoder and text encoder project their outputs into a shared latent space; the dot product between an image feature and a text feature produces a similarity score. Hugging Face’s CLIP documentation says the model was trained on 400M image-text pairs. That training figure describes the model’s background, not the speed of inference on any particular device.
Moving the same model between a CPU, GPU, and NPU changes execution characteristics rather than the embedding definition. A target may reduce latency, increase sustained throughput, lower power use, or simplify deployment, but only if the runtime supports the graph and the resulting vectors remain accurate enough for the application.
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The deployment stack: model, preprocessing, runtime, and device
Think of an embedding service as four linked layers:
- Model graph: the network and its exported format, such as ONNX or an OpenVINO intermediate representation.
- Preprocessing: image decoding, color conversion, resize or crop, channel ordering, scaling, normalization, and tensor dimensions.
- Runtime: the inference API that loads the graph and chooses an execution backend.
- Device backend: CPU, GPU, NPU, or a heterogeneous combination supplied by the runtime and its provider libraries.
A failure or performance regression at any layer can look like a hardware problem. For example, decoding large JPEGs on the host can dominate an otherwise fast accelerator, while an unsupported operator can silently execute on the CPU and make a nominal GPU or NPU deployment slower than expected.
Preserve the model’s preprocessing contract
Use the processor associated with the selected model whenever possible. Hugging Face’s CLIP documentation assigns resizing or rescaling and normalization to the processor. Those operations are not optional conveniences: they establish the input distribution on which the encoder was trained.
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- Image color mode and channel order, such as RGB rather than an accidental BGR tensor.
- Resize and crop policy, including the final height and width.
- Pixel scaling and mean or standard-deviation normalization.
- Tensor layout, data type, and batch dimension.
- Any postprocessing required by the application, such as L2 normalization before nearest-neighbor search.
Test representative images before comparing devices. Include portrait and landscape images, unusual aspect ratios, transparent images, grayscale inputs, very small files, and high-resolution photographs. A pipeline that works on a square RGB sample can still fail or produce inconsistent vectors when production images follow a different distribution.
Choose an inference path
ONNX Runtime execution providers
ONNX Runtime exposes one application API while assigning supported graph nodes or subgraphs to execution providers. Its documented provider ecosystem includes the default CPU provider, NVIDIA CUDA and TensorRT, DirectML, Intel OpenVINO, AMD MIGraphX, Qualcomm QNN, CoreML, and other providers. Provider status is not uniform; some entries are marked preview or deprecated. A provider package may also require separate vendor libraries, drivers, or runtime components.
Provider order matters. ONNX Runtime uses the listed priority to decide where supported work should run, and unsupported portions can be assigned elsewhere. Registering a GPU provider therefore does not prove that every operator executes on the GPU. Inspect the registered providers, provider options, runtime logs, and profiling output, then verify that unexpected graph regions have not fallen back to the CPU.
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The ONNX Runtime tutorial that converts a PyTorch ResNet-50 example to ONNX and runs it with CPU, CUDA, or Intel OpenVINO demonstrates portability of the application pattern. It is not a comparative image-embedding speed test, so use your own model and workload for performance decisions.
OpenVINO for CPU, GPU, and NPU targets
OpenVINO documents CPU, GPU, and NPU execution and includes automatic device selection, heterogeneous execution, and automatic batching features. Heterogeneous execution can place unsupported operations on another device, which improves compatibility but changes the performance profile you are measuring.
OpenVINO’s feature matrix identifies important NPU limitations in its documented snapshot: NPU automatic batching is unavailable, and dynamic shapes are not supported there. Confirm the current hardware, driver, toolkit release, supported operators, and model-shape requirements before relying on NPU compilation. A model that compiles for an OpenVINO CPU may still require graph changes or fixed dimensions for an NPU.
DirectML on Windows
ONNX Runtime’s DirectML documentation says DirectML remains supported but is in sustained engineering; new Windows-based ONNX Runtime development has moved to WinML. The documented DirectML provider requires a DirectX 12-capable device and supports ONNX opset versions up to 20 with specified exceptions. Treat those limits as compatibility checks, not as a guarantee that a particular graphics processor will deliver a desired latency.
Runtime and device comparison
| Deployment option | What the documented path provides | Constraints to verify |
|---|---|---|
| ONNX Runtime with CPU provider | Common application API and CPU execution for supported ONNX operators | Model operators, graph partitioning, thread settings, and host preprocessing cost |
| ONNX Runtime with CUDA or TensorRT | GPU execution providers for supported NVIDIA workloads | Provider libraries, drivers, exported graph compatibility, and unsupported-node fallback |
| ONNX Runtime with OpenVINO provider | ONNX Runtime API backed by Intel OpenVINO capabilities | OpenVINO installation, device support, operator coverage, and provider priority |
| OpenVINO directly | Explicit CPU, GPU, NPU, automatic selection, heterogeneous execution, and automatic batching features | NPU shape and operator limits, current drivers, compilation success, and feature availability |
| ONNX Runtime DirectML | Windows GPU path using a DirectX 12-capable device | DirectML’s sustained-engineering status, opset support through 20 with exceptions, and current WinML guidance |
A repeatable setup workflow
1. Define the embedding job and acceptance criteria
Write down what the vector will do: image-to-image retrieval, text-to-image matching, duplicate detection, clustering, or another task. Decide whether you need one request at a time, a queue of independent images, or concurrent service requests. Set separate targets for single-image latency and sustained throughput, and define an embedding-quality baseline such as recall at a chosen rank or agreement with a reference similarity score.
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2. Establish a reference implementation
Run the original model with its supported processor on a known-good configuration. Save embeddings for a representative validation set and record the exact model revision, input dimensions, preprocessing values, output data type, and any vector normalization. This reference is the comparison point for every export, quantization, or device change.
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3. Export or load a compatible graph
Choose an ONNX graph when you need one graph to move among ONNX Runtime providers. Choose an OpenVINO representation when direct OpenVINO features or device compilation are central to the deployment. Check operator support and the ONNX opset or conversion requirements for the intended backend before spending time on tuning.
4. Keep dimensions static when the workload permits
If production images can be normalized to one tensor size, a static-shape graph simplifies compilation and can improve CPU runtime optimizations. OpenVINO’s CPU guidance specifically notes that static model shapes may improve performance when input dimensions remain fixed. Fixed shapes are especially important to investigate for NPU deployment because the documented NPU matrix does not support dynamic shapes.
5. Install and register the provider
Install the runtime package and the provider-specific libraries and drivers required by the selected backend. At application startup, enumerate the registered providers and record their order and options. Fail clearly when the intended provider is absent instead of silently accepting a CPU-only fallback in a production service.
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Enable runtime logging or profiling where available. Look for graph partitioning, unsupported operators, memory-copy boundaries, and transfers between host and accelerator. A model can report successful inference while running only a small supported subgraph on the accelerator. Validate execution placement with the runtime’s diagnostic output rather than inferring it from the name of the installed package.
7. Measure the complete pipeline
Measure at least two boundaries:
- End-to-end request latency: image decode, preprocessing, device transfer, inference, output conversion or normalization, and any required storage or similarity-search step.
- Steady-state inference throughput: repeated requests after warm-up, reported with batch size, concurrency, input dimensions, precision, and a latency target.
Also record cold-start and model-load or compile time when the service scales frequently. OpenVINO’s benchmark guidance distinguishes latency from throughput and emphasizes that results depend on workload parameters. The benchmark page’s published toolkit figures are based on release 2024.6 and dated December 18, 2024; they are not a current, universal image-embedding ranking.
8. Validate vectors and task quality
Compare vectors from the candidate backend with the reference output using a tolerance appropriate to the data type, then evaluate the actual retrieval or similarity task. A small numerical difference can be harmless, while a larger difference in nearest neighbors can be unacceptable. Repeat this check after graph optimization, quantization, half-precision conversion, or provider changes.
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Precision, shapes, and batching trade-offs
Half precision and reduced precision
OpenVINO documents that some CPUs convert f16 intermediate-representation values to f32 internally, while CPUs with native half-precision support may use bf16 or f16 for performance. Lower precision can reduce memory traffic and improve speed on supported targets, but reduced mantissa precision can change inference accuracy. Treat precision as an experiment: measure both performance and embedding quality, and keep the reference configuration available for rollback.
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Dynamic dimensions are convenient when images arrive at varying sizes, but they can reduce optimization opportunities and are not available for NPU execution in the cited OpenVINO feature matrix. A practical compromise is to resize images in preprocessing and export one or a small number of fixed shapes. If multiple fixed shapes are necessary, benchmark each separately because compilation and memory behavior can differ.
Batching and concurrency
Batching combines independent images into one inference call and can improve throughput, but it may increase the latency of an individual request and memory use. OpenVINO documents automatic batching for some devices while marking it unavailable on NPU in the referenced matrix. For services that prioritize responsiveness, use a bounded queue and measure tail latency rather than optimizing only average throughput.
On CPU, OpenVINO’s guidance describes multiple execution streams for concurrent requests. Streams are useful when requests are independent, but adding concurrency beyond the available cores or memory bandwidth can increase latency. Tune with the same image size and request mix used in production.
How to compare CPU, GPU, and NPU fairly
| Axis | Measurement or question |
|---|---|
| Compatibility | Do the model operators, ONNX opset or conversion path, operating system, drivers, and runtime release support the target? |
| Execution placement | Which nodes run on the requested device, and which fall back to another provider? |
| Embedding quality | Do vectors and downstream retrieval or similarity results remain within the agreed tolerance of the reference? |
| Single-request latency | What is the time from decoded image or ready tensor to usable embedding, and what percentile matters? |
| Throughput | How many embeddings per second are sustained at the required batch size and concurrency while meeting the latency target? |
| Operations | How much memory, model-load time, compile time, power, driver maintenance, and fallback risk does the configuration add? |
Run every configuration with identical model weights, preprocessing, input dimensions, precision, warm-up procedure, and measurement boundaries. Report whether the test is single-item or batched and whether downstream indexing is included. There is no controlled cross-vendor image-embedding statistic in the cited material, so do not publish a universal speedup, efficiency claim, or device ranking from these sources.
Choosing a practical target
Use a CPU when simplicity or portability dominates
A CPU path is a strong baseline when the model is modest, traffic is intermittent, the deployment already has CPU capacity, or installing accelerator drivers would add more operational risk than value. It also provides a reliable fallback for unsupported operators and a reference for validating other backends. Improve results by fixing shapes, limiting unnecessary image conversions, and tuning concurrency only after measuring the complete pipeline.
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Use a GPU when parallel demand is established
A GPU is worth evaluating when many independent images can be processed concurrently, the selected provider fully supports the graph, and the workload can keep the device busy. Account for host-to-device transfer, batching delay, memory capacity, provider libraries, and cold-start costs. A GPU that accelerates matrix operations can still lose end-to-end if decoding and preprocessing remain serial on the host.
Use an NPU when its platform integration matches the graph
An NPU can be attractive for supported edge or client hardware where power and sustained local inference matter. The deployment must satisfy platform-specific drivers, supported operators, fixed-shape requirements, and compilation constraints. Because the documented NPU feature matrix lacks automatic batching and dynamic shapes, an NPU is not a drop-in replacement for every ONNX model or service pattern.
Troubleshooting common failures
The intended provider is missing
Check the installed runtime package, provider-specific libraries, operating-system support, and device drivers. Print the provider list at startup and verify that the application is using the package you configured rather than a CPU-only installation.
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Inference succeeds but is unexpectedly slow
Inspect profiling and logs for unsupported operators, CPU fallback, repeated device transfers, unbatched requests, and preprocessing outside the timed region. Compare ready-tensor latency with end-to-end latency to locate the dominant stage.
An NPU model will not compile
Try a fixed input shape, confirm that every operator is supported by the NPU version and driver, and check the current OpenVINO NPU requirements. If heterogeneous execution is enabled, identify which operations are being assigned elsewhere and decide whether that split is acceptable.
Vectors differ after optimization
First compare preprocessing tensors, not just final vectors. Then check output data type, normalization, graph rewrites, and precision settings. Re-run the task-level retrieval or similarity test; numerical agreement alone does not establish that application quality is preserved.
Throughput is high but user latency is poor
Separate queueing delay from inference time, reduce batch size or concurrency, and track tail percentiles. Automatic batching or large queues can raise embeddings-per-second while making individual requests wait too long.
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Deployment checklist
- Model revision and output dimensions are recorded.
- Processor settings match the model’s resize, crop, channel, scaling, and normalization contract.
- Representative and unusual image formats have been tested.
- Exported graph, opset, operators, and fixed or dynamic shapes are compatible with the chosen provider.
- Required drivers and provider libraries are installed and versioned.
- Startup diagnostics confirm the intended provider and reveal fallback partitions.
- Cold-start, single-request latency, steady-state throughput, memory, and tail latency are measured.
- Vectors and downstream retrieval or similarity quality are compared with a reference after every optimization.
- Fallback behavior is deliberate, observable, and acceptable for the service’s quality and latency targets.
Bottom line
Start with a correct CPU reference, preserve preprocessing, and make provider placement observable. Then test the same fixed workload through the most compatible GPU or NPU backend available on your platform. Select the configuration that meets both your measured latency or throughput target and your embedding-quality requirement; the hardware label alone cannot make that decision.
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