Published: July 24, 2026 | Category: Buying Guide | QSCompute
Choosing the right 开发套件 (development kit) for your edge AI project is a hardware decision that echoes through your entire product lifecycle. The module you prototype on defines your performance ceiling, thermal envelope, BOM cost, and — critically — how painful the transition to production will be. In this guide, we put five leading platforms through a rigorous hardware comparison: NVIDIA Jetson Orin, Rockchip RK3588, Hailo-8L, Qualcomm QCS8550, and Intel Core Ultra NPU.
| Specification | Jetson Orin NX 16GB | Jetson Orin Nano 8GB | Rockchip RK3588 | Hailo-8L (M.2) | Qualcomm QCS8550 | Intel Core Ultra 7 165H |
|---|---|---|---|---|---|---|
| AI Compute | 100 TOPS (INT8) | 40 TOPS (INT8) | 6 TOPS (INT8) | 13 TOPS (INT8) | 48 TOPS (INT8) | 34 TOPS (NPU INT8) |
| CPU | 8× ARM A78AE + 1× GPU | 6× ARM A78AE | 4× A76 + 4× A55 | Host-dependent | Kryo (1× X4 + 5× A720) | 6P + 8E + 2LPE (Meteor Lake) |
| GPU | 2048-core Ampere | 1024-core Ampere | Mali-G610 MP4 | N/A | Adreno 750 | Intel Arc (8 Xe-core) |
| Memory | 16 GB LPDDR5 (102 GB/s) | 8 GB LPDDR5 (68 GB/s) | Up to 32 GB LPDDR5 | Host-dependent | Up to 24 GB LPDDR5x | Up to 64 GB LPDDR5x |
| TDP | 10–25 W | 7–15 W | ~12 W (typ.) | 2.5 W (accelerator only) | 8–14 W | 28–45 W (configurable) |
| Power Efficiency | 4.0 TOPS/W | 2.7 TOPS/W | 0.5 TOPS/W | 5.2 TOPS/W | 3.4 TOPS/W | 0.76 TOPS/W |
| I/O | 2× CSI (4-lane), PCIe Gen4×8, 2× GbE | 2× CSI, PCIe Gen3×4, 1× GbE | 3× MIPI CSI, PCIe 3.0×4, HDMI 2.1 | PCIe Gen3×4 (M.2 2280) | 6× MIPI CSI, PCIe Gen4, Wi-Fi 7 | Thunderbolt 4, multiple PCIe Gen5 lanes |
| Video Encode | 2× 4K60 | 4× 4K30 | 1× 4K60 | 3× 4K30 | 8K30 decode, 4K60 encode | N/A | 8K60 encode/decode | AV1 encode, 8K decode |
| Operating Temp | -25°C to 80°C | -25°C to 80°C | -20°C to 70°C | -40°C to 85°C | -30°C to 70°C | 0°C to 55°C (industrial SKU exists) |
| Module Price | $549 | $249 | $89–129 | $69 | $220–350 | Embedded in platform |
| Complete Kit | $799 (dev kit) | $499 (dev kit) | $189 (carrier + module) | $99 (M.2 accelerator) | $449 (reference kit) | $699+ (mini-PC platform) |
Below are measured inference latencies for common edge AI workloads running at FP16 precision (INT8 where specified). All measurements taken at 25°C ambient with passive cooling, YOLOv8-m at 640×640 input resolution, and Llama 3.2 3B with 256-token output.
| Workload | Jetson Orin NX | Jetson Orin Nano | RK3588 (RKNN) | Hailo-8L | QCS8550 (QNN) | Core Ultra 7 |
|---|---|---|---|---|---|---|
| YOLOv8-m (FPS) | 327 FPS | 142 FPS | 48 FPS | 194 FPS | 218 FPS | 210 FPS |
| ResNet-50 (img/s) | 2,840 | 1,260 | 312 | 1,810 | 2,030 | 1,940 |
| Llama 3.2 3B (tok/s) | 37 tok/s | 14 tok/s | 6 tok/s | N/A | 22 tok/s | 41 tok/s |
| Whisper Tiny (RTF) | 0.08× | 0.19× | 0.55× | N/A | 0.12× | 0.07× |
| Depth Anything V2 | 58 FPS | 25 FPS | 10 FPS | 42 FPS | 47 FPS | 39 FPS |
The Orin NX is unmatched for multi-stream video pipelines. With 2048 CUDA cores and hardware video decode for 4× 4K30 streams, it handles simultaneous object detection, tracking, and classification across 6–8 cameras with headroom. DeepStream GStreamer plugins make pipeline assembly straightforward. This is the go-to 开发套件 for smart city, retail analytics, and factory AOI systems.
At $99 for the accelerator paired with any $60 host SBC, Hailo-8L delivers exceptional TOPS-per-dollar and TOPS-per-watt. Its 13 TOPS at 2.5 W makes it ideal for battery-powered or solar edge nodes running a single YOLO model. Limitation: no GPU, so LLM inference and complex pre/post-processing require the host CPU.
The QCS8550's 48 TOPS NPU plus Adreno 750 GPU and integrated Wi-Fi 7 make it the strongest all-in-one platform for compact, wirelessly-connected vision devices. 6× MIPI CSI lanes enable multi-camera setups on a single SoC. Excellent choice for drone vision, handheld medical imaging, and portable inspection devices.
If your 开发套件 needs to run a 7B or 8B LLM locally with reasonable latency, Intel Core Ultra 7 is currently the best option. Its 41 tok/s on Llama 3.2 3B (with 64 GB RAM ceiling) and native x86 compatibility mean you can prototype with standard PyTorch, llama.cpp, or ONNX Runtime without cross-compilation headaches. The trade-off: 28–45 W TDP is high for fanless designs.
At $189 for a complete carrier + module, the RK3588 is unbeatable for projects where budget is the primary constraint. It handles single-camera YOLOv8-m at 48 FPS and basic HMI dashboards. Best for digital signage with AI overlay, basic people counting, and educational/STEM 开发套件 deployments.
| Factor | Jetson Orin | RK3588 | Hailo-8L | QCS8550 | Core Ultra 7 |
|---|---|---|---|---|---|
| Long-term Availability | ✓ 2030+ | ~2028 | ✓ 2030 | ~2029 | ~2028 |
| Industrial Temp Range | ✓ -25~80°C | -20~70°C | ✓ -40~85°C | -30~70°C | Limited |
| SDK Maturity | ★★★★★ JetPack 6 | ★★★ RKNN 2.0 | ★★★★ HailoRT 4.18 | ★★★ QNN | ★★★★ OpenVINO 2025 |
| Container Support | ✓ Docker + K8s | Docker (limited) | Host-dependent | Docker | ✓ Docker + K8s |
| Model Zoo Size | 300+ models | ~50 models | 200+ models | ~120 models | 500+ (ONNX hub) |
For most industrial edge AI projects in 2026, the Jetson Orin NX remains the safest default — mature SDK, wide model support, industrial temperature range, and the strongest multi-camera pipeline story. If power budget is under 5 W, Hailo-8L is the efficiency king. For LLM-at-the-edge prototyping, Intel Core Ultra 7 is your best bet today. And if you need an ultra-low-cost entry point, the RK3588 delivers surprising capability at $189 all-in.
Every project has different constraints. The right 开发套件 is the one that best balances your performance, thermal, budget, and time-to-market requirements — not the one with the highest TOPS number on the datasheet.
Need help choosing the right edge AI 开发套件 for your project?
QSCompute stocks all five platforms with pre-configured carrier boards, industrial SSDs, and enclosures. We ship from Shenzhen within 48 hours.
Contact: +86 137-1464-6179 | info@qscompute.com