Compare edge AI accelerators by running your intended model on the complete system under realistic conditions—not by ranking peak TOPS figures. A useful comparison fixes the workload first, then measures sustained latency and throughput, system power and thermal behavior, usable memory, software compatibility, and integration and lifecycle constraints.
1. Define the workload before comparing hardware
Performance is meaningful only in relation to a specific job and service target. Record the same workload details for every device you evaluate:
- Model and version: include the model architecture and any modifications.
- Precision and quantization: record the format actually deployed, such as INT8, and whether sparsity is used.
- Input shape: specify image resolution, sequence length, or other relevant dimensions.
- Batch and concurrency: state batch size and the number of simultaneous streams, requests, or pipelines.
- Service target: define required throughput and latency, including tail latency when occasional slow responses matter.
- Accuracy requirement: ensure any quantized or converted model still meets the application’s acceptable accuracy.
Peak TOPS or TFLOPS is a vendor-reported compute specification, not an application speed measurement. Even two figures with the same unit may describe different precision, product classes, and conditions. Do not use them to predict your workload’s frame rate or rank platforms as if they were equivalent tests.
2. Measure sustained performance under disclosed conditions
Run the same representative inputs and software path on each candidate. Record latency, sustained throughput, and the number of concurrent workloads the system can handle while meeting its service target. Include warm-up and steady-state results where relevant, and note whether reported latency is an average or a tail percentile.
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Keep the test conditions alongside every result: model, precision, sparsity, batch size, input dimensions, software and driver versions, power mode, cooling, host system, and whether the number is a peak specification or a measured result. A benchmark without those details is difficult to reproduce and may not describe your deployment.
Vendor comparison charts need the same scrutiny. Hailo’s Hailo-8 Century product page says its Evaluation Platform results were measured at room temperature for INT8, while its NVIDIA T4 comparator is a peak INT8 figure using sparsity and batch size 8. Those conditions do not form a like-for-like general comparison. See the Hailo-8 Century product specifications and benchmark qualifications.
3. Compare power at the boundary that matters
Distinguish among an accelerator’s or card’s TDP, a module’s configured power mode, and the draw of the complete system. They are not interchangeable. For an edge deployment, measure power at the relevant input boundary—such as the board or system input—and state what is included, including the host, memory, storage, and cooling if those contribute to the reading.
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Measure during sustained inference in the target enclosure and cooling environment. Record average and peak power, temperature, cooling method, and throughput after the system reaches thermal equilibrium. Thermal controls can change clocks and sustained output, so a short run on an open bench may not represent a passively cooled or enclosed installation.
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NVIDIA’s archived Jetson Linux r36.4 guide to platform power and performance documents power modes, thermal management, hardware throttling, thermal shutdown, and software power modeling. This is a concrete reminder to treat power and thermal behavior as system properties rather than inferring energy use from a compute rating.
If energy per inference or cost per useful result matters, calculate it from measured power and completed work at the required service level. Do not derive FPS/W or energy per inference by combining a vendor’s unrelated TOPS and watt figures.
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4. Check memory capacity, bandwidth, and topology
Confirm that the model fits in usable memory with room for the runtime and the workload around it. Account for weights, activations, caches, preprocessing and postprocessing, and any simultaneous pipelines. Then test the largest stable batch or concurrency you need; a model that loads successfully may still exceed practical memory limits under real traffic.
Record memory capacity, bandwidth, type, and topology. In particular, distinguish shared system memory from memory attached to a discrete accelerator, and account for memory consumed by the host operating system and other processes. Capacity determines whether a workload fits; bandwidth and data movement can constrain performance even when capacity is sufficient.
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As examples of product-specific capacity—not a performance ranking—the current NVIDIA Jetson lineup page lists Jetson AGX Thor with 128 GB of memory, Orin NX variants with 8 GB or 16 GB, and Orin Nano variants with 4 GB or 8 GB. Verify the exact module and configuration on the NVIDIA Jetson modules and lineup page.
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5. Verify that the software path supports your actual model
Before selecting hardware, validate the precise model and deployment path. Check whether its operators and precision are supported, whether conversion or compilation changes the graph, and which framework, runtime, driver, and operating system versions are required. Also test the model-update process: a platform that runs today’s model but makes future updates difficult may be a poor long-term fit.
- Model coverage: confirm operators, custom layers, dynamic shapes, and preprocessing requirements.
- Precision and conversion: verify supported quantization formats and check accuracy after conversion.
- Toolchain: identify the framework versions, compiler or converter, runtime, drivers, and OS required in deployment.
- Maintenance: determine how models are packaged, updated, rolled back, and monitored in the field.
Vendor ecosystem descriptions are starting points, not proof that every model runs unchanged. NVIDIA describes JetPack as its Jetson development and deployment suite; Intel describes OpenVINO optimization across CPU, GPU, and NPU; Hailo lists TensorFlow, TensorFlow Lite, Keras, PyTorch, and ONNX support for the Century card. Test your exact graph on the intended release and device.
For platform context, see NVIDIA’s Jetson lineup and ecosystem information, Intel’s Edge AI and Edge Computing overview, and the Hailo-8 Century product page.
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6. Compare integration, lifecycle, and cost per useful result
The accelerator is only one part of an edge system. Check the host interface and slot availability, board or carrier availability, camera and sensor I/O, physical size, ruggedness, cooling requirements, deployment tooling, and lifecycle and support terms. These can rule out a device that looks attractive on compute alone.
Include the complete system cost and, where relevant, measured energy or cost per inference at your target service level. Compare equivalent systems doing equivalent work; a component price or peak throughput does not establish the cost of delivering the same application result.
A 2026 comparative study by Davide Baltieri and Tobia Peruzzi of Covision Lab evaluated ten accelerators spanning ASIC NPUs, SoC DSPs, and integrated NPUs, using twelve reference models across convolutional, mobile, and transformer architectures and an NVIDIA RTX A5000/TensorRT baseline. It examines throughput, latency, model compatibility, power efficiency, SDK maturity, and product lifecycle. Its findings are specific to the tested devices, software, and workloads, not a universal cross-platform winner. Read NPU Hardware Evaluation v1.0: A Comparative Study of Edge AI Inference Accelerators.
7. Use vendor specifications as context, not as a normalized ranking
The following figures illustrate the variety of specifications vendors publish. They describe different products and conditions; they are not comparable benchmark results. NVIDIA and Intel figures below are current vendor specifications accessed in 2026. Hailo’s numbers are from its product page and depend on the listed model or configuration.
| Platform example | Published specification | How to interpret it |
|---|---|---|
| NVIDIA Jetson AGX Thor | Up to 2,070 FP4 TFLOPS; 128 GB memory; configurable 40–130 W | Vendor module-lineup specification. FP4 TFLOPS, memory capacity, and configurable power range are distinct attributes, not an independent workload result. |
| NVIDIA Jetson AGX Orin | Up to 275 TOPS | Vendor module-lineup specification; confirm the precise module and workload conditions before estimating application performance. |
| NVIDIA Jetson Orin NX | Up to 157 TOPS | Vendor module-lineup specification; available configurations differ, including 8 GB and 16 GB variants. |
| NVIDIA Jetson Orin Nano | Up to 67 TOPS; 7–25 W power options | Vendor module-lineup specification. The power options are not whole-system draw or energy per inference. |
| Intel Core Ultra Series 3 for Edge | Up to 180 platform TOPS | Vendor platform specification; benchmark the exact SKU, model, and software stack. |
| Hailo-8 Century | 52–208 TOPS across listed models; maximum TDP is 15–45 W or 45–75 W depending on listed card configuration | Use the exact product-model and interface row on the vendor page; do not treat the family range as one card’s rating. |
| Hailo-8 Century benchmark claim | 400 FPS/W on the ResNet50 benchmark model | Vendor benchmark statement for that model; it does not establish efficiency for another workload. |
Sources: NVIDIA Jetson modules and lineup, Intel Edge AI and Edge Computing, and Hailo-8 Century. Product specifications, software support, and availability can change; verify the exact SKU and software release you intend to deploy.
8. A practical comparison workflow
- Freeze the workload. Choose the model and version, input shape, precision, batch, concurrency, accuracy floor, and latency and throughput targets.
- Shortlist feasible systems. Filter by memory, host interface, I/O, size, cooling, operating environment, and software requirements before benchmarking.
- Build the intended software path. Use the target framework conversion, compiler, runtime, drivers, and OS. Validate model coverage and accuracy on each candidate.
- Run sustained tests. Use representative inputs and traffic, measure latency and throughput after warm-up, and include tail latency when required by the service target.
- Measure power and thermals. Record the measurement boundary, power mode, average and peak draw, temperatures, cooling, and results after thermal equilibrium in the intended enclosure.
- Stress memory and concurrency. Check actual usable capacity and stable operation with the full pipeline and required simultaneous workloads.
- Compare complete-system outcomes. Review measured performance, power, memory headroom, integration effort, lifecycle, and total cost at the same service level. Keep test conditions with every number.
This process yields a workload-specific decision rather than a universal “best accelerator.” The right choice depends on the model, power ceiling, memory needs, physical system constraints, software path, and required support horizon.
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