Embedded SBC AI Benchmark 2026 — Jetson Orin vs Rockchip RK3588 vs TI Jacinto vs NXP i.MX for Edge Inference

The 嵌入式 SBC landscape for AI inference has never been more competitive. In 2026, four platforms dominate the conversation: NVIDIA Jetson Orin (CUDA + TensorRT), Rockchip RK3588 (RKNN NPU), TI Jacinto TDA4VM (C7x DSP + MMA), and NXP i.MX 95 (eIQ Neutron NPU). Each takes a fundamentally different approach to AI acceleration, and the "best" choice depends entirely on your workload, software ecosystem, and volume pricing. This benchmark puts them head-to-head on real tasks — computer vision, NLP, and multi-model pipelines — with measured power draw at the wall.

Platform Overview

Jetson Orin AGX 64GB (275 TOPS, $1,599) excluded from this comparison as it plays in a different price tier. This benchmark focuses on the sub-$600 嵌入式 SBC range where most production volume deployments live.

AI Inference Benchmarks

All benchmarks use the respective platform's optimized runtime: TensorRT 8.6 (Jetson), RKNN (RK3588), TIDL (TI), and eIQ Neutron (NXP). Batch size = 1, power measured at DC input.

Computer Vision: YOLOv8n (640×640, FP16/INT8)

Takeaway: Jetson Orin NX dominates raw throughput — 6× faster than RK3588 and 8× faster than TDA4VM. But for single-camera inspection at 30 FPS, the RK3588 is already overkill at 68 FPS. The real question isn't "which is fastest" but "which meets your FPS requirement at the lowest power and cost."

Classification: ResNet-50 v1.5 (224×224, FP16/INT8)

NLP: Llama 3.1 8B (INT4 Quantized, TensorRT-LLM / llama.cpp)

Critical finding: Only Jetson Orin NX 16GB can run Llama 3.1 8B at usable speeds for edge deployment (18.5 tokens/sec — barely conversational). All other platforms under $600 either lack sufficient RAM or NPU support for transformer models. If edge LLM is your use case, Jetson is currently the only game in town.

Price-Performance Analysis

TOPS alone is misleading. Jetson's TensorRT optimization extracts far more real-world throughput per theoretical TOPS than the NPU-based competitors. The RK3588's 6 TOPS delivers only 6.8% of the Orin NX's YOLOv8 throughput — despite being 6% of the TOPS count. The software stack matters as much as the silicon.

Software Ecosystem: The Hidden Cost

The Jetson software advantage is hard to overstate: a developer can take a PyTorch model from HuggingFace, export to ONNX, and get an optimized TensorRT engine in under an hour. The RK3588 requires navigating RKNN-Toolkit2's model compatibility matrix — many ops simply aren't supported (e.g., certain attention mechanisms, dynamic shapes). TI and NXP have longer toolchains and smaller communities.

Decision Flowchart

• Need to run an LLM or transformer at the edge? → Jetson Orin NX 16GB. Nothing else under $600 can do it.
• Single-camera inspection, cost-sensitive, volume >1,000 units? → Rockchip RK3588. $149/unit, 68 FPS YOLOv8, proven in production NVR deployments.
• Functional safety required (ISO 26262 ASIL-B/D)? → TI TDA4VM. The only platform here with automotive safety certification pedigree. Higher cost, but mandatory for ADAS and safety-critical industrial.
• Ultra-low-power always-on sensor node (<5W)? → NXP i.MX 95. 5.1W for YOLOv8 inference. Battery/solar friendly for remote deployments.
• Prototyping and getting to market fast? → Jetson Orin Nano 8GB. $249, massive community, best docs, fastest model-conversion turnaround.

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