Edge AI systems today are expected to handle a wide range of workloads—from real-time computer vision to running compact large language models (LLMs) directly on embedded devices. Instead of relying solely on the NPU integrated into the SoC, many engineers now use M.2 AI accelerator modules to scale AI performance without redesigning the underlying hardware platform.
However, choosing the right accelerator is not straightforward. Different chips are optimized for different workloads: some excel at high-FPS vision inference, while others are designed to support transformer models and generative AI.
This article compares several widely used AI accelerators—Hailo H8, DeepX DX-M1, MemryX MX3, Kinara Ara-2, RK1820, and RK1828—to help engineers understand their strengths, architectural differences, and ideal use cases.
AI Accelerator Hardware Comparison
These modules range from ultra-efficient vision processors to hybrid chips capable of running LLMs locally. The table below summarizes key architectural characteristics and deployment considerations across the six accelerators.
| Comparison Dimension | Hailo H8 | DeepX DX-M1 | Memryx MX3 | Kinara Ara-2 | RK1820 | RK1828 |
|---|---|---|---|---|---|---|
| AI Performance | 26 TOPS (INT8) | 25 TOPS (INT8) | 24 TOPS (4-chip architecture) | 40 TOPS (INT8) | 20 TOPS (INT8) | 20 TOPS (INT8) |
| CPU | – | – | – | – | 3×RISC-V 64-bit + FPU | 3×RISC-V 64-bit + FPU |
| Power | 2.5W / Peak 8.65W | 2~5W | 8~10W / Peak 14W | 12W | <5W (1.5mW in power-saving mode) | <5W (1.5mW in power-saving mode) |
| AI Efficiency (TOPS/W) | ~10.4 TOPS/W | ~5-12.5 TOPS/W | ~2.4-3 TOPS/W | ~3.3 TOPS/W | ~4-13 TOPS/W | ~4-13 TOPS/W |
| M.2 Form Factor | 22×42/60/80mm optional | 22×80mm (2280) | 22×80mm (2280) | 22×80mm (2280) | 22×80mm (2280) + 12V auxiliary power | 22×80mm (2280) + 12V auxiliary power |
| Interface Type | PCIe Gen3 ×4 | PCIe Gen3 ×4 | PCIe Gen3 ×2 | PCIe Gen4 ×4 | PCIe 2.0 ×1 / USB 3.0 | PCIe 2.0 ×1 / USB 3.0 |
| Onboard Memory | Host-dependent | 4GB LPDDR5 + 1GB NAND | 4-chip shared + expandable | 16GB LPDDR4(X) | 2.5GB 3D stacked DRAM | 5GB 3D stacked DRAM |
| Memory Bandwidth | ~32GB/s (PCIe) | ~32GB/s (PCIe) | ~16GB/s (PCIe×2) | ~64GB/s (PCIe Gen4) | ~1TB/s (3D stacked) | ~1TB/s (3D stacked) |
| Supported Models | YOLO/ResNet/MobileNet | YOLOv5/Custom DNN | ONNX cascading inference | LLaMA-2/Stable Diffusion | ≤3B parameter LLM | ≤7B parameter LLM |
| Precision | INT8/INT16 | INT8 | INT8/INT16 | INT4/INT8/FP16 | INT4/8/16/FP8/16/BF16 | INT4/8/16/FP8/16/BF16 |
| Supported Frameworks | TF/PyTorch/ONNX/Keras | TF/PyTorch/ONNX | ONNX/PyTorch/TF | TF/PyTorch/ONNX/Caffe | TF/PyTorch/Caffe/ONNX/TFLite | TF/PyTorch/Caffe/ONNX/TFLite |
| OS Support | Linux/Windows | Linux | Linux (ARM/x86/RISC-V) | Linux/Windows | Linux | Linux |
| Operating Temperature | -40~85°C | -25~85°C | -40~85°C | 0~70°C | -20~60°C (kit spec) | -20~60°C (kit spec) |
| Price | $179~282 | ~$147-159 | ~$149 | ~$299 | ~$140-200 (module estimate) | ~$280-340 (module estimate) |
| Software Ecosystem | Hailo DFC+Runtime | DX-RT SDK+Open-source Drivers | NeuralCompiler+Python API | Kinara SDK+Compiler | RKNN3 Toolkit+Runtime | RKNN3 Toolkit+Runtime |
| Key Features | Multi-stream concurrency + ultra-low latency | 20× better FPS/W than GPUs | 4-chip cascading + dataflow architecture | 40 TOPS compute + 16GB memory + large model optimization | 3D stacked bandwidth + 3B LLM support | 3D stacked bandwidth + 7B LLM support |
| Cooling Requirement | Low (2.5W typical) | Low (2-5W) | Medium (3CFM@70°C required) | Medium (active cooling for 12W) | Heatsink + 12V auxiliary power | Heatsink + 12V auxiliary power |
Key Observations:
- Power efficiency varies widely. Hailo H8 and DeepX DX-M1 target low-power edge devices, while Kinara Ara-2 delivers higher compute at higher power.
- Memory architecture differs. Some modules rely on host memory via PCIe, while others integrate onboard memory to reduce data transfer overhead.
- Stacked DRAM matters for LLMs. RK1820 and RK1828 leverage 3D stacked memory to achieve extremely high internal bandwidth for transformer workloads.
Edge LLM Inference Capability
Generative AI is moving closer to the edge, making local LLM inference increasingly relevant. The table below compares approximate LLM capability.
| Product | Max Supported Model | Quantization | Typical Inference Speed (7B Model) | Memory Bottleneck |
|---|---|---|---|---|
| RK1828 | ✅ 7B parameters | INT4/INT8/w4a16 | ~15-30 tokens/s (Qwen2.5) | 5GB on-module DRAM (sufficient) |
| RK1820 | ✅ 3B parameters | INT4/INT8 | ~40-60 tokens/s (3B model) | 2.5GB requires model pruning |
| Kinara Ara-2 | ✅ 7B parameters | INT4/INT8/FP16 | ~12 tokens/s (LLaMA-2) | 16GB LPDDR4X (ample) |
| Hailo H8 | ❌ CNN-focused | INT8/INT16 | – | Relies on host memory |
| DeepX DX-M1 | ❌ Lightweight DNN only | INT8 | – | 4GB LPDDR5 (sufficient) |
| Memryx MX3 | ❌ CNN pipeline-focused | INT8/INT16 | – | Needs external expansion |
Practical insights:
- RK1828 can run 7B-class LLMs locally with INT4 quantization.
- RK1820 targets smaller models (≤3B parameters) but can deliver higher token throughput due to reduced memory pressure.
- Kinara Ara-2 provides flexibility due to its large 16GB memory capacity.
- Vision-oriented accelerators like Hailo H8, DeepX DX-M1, and MemryX MX3 are generally optimized for CNN-based workloads rather than LLM inference.
Quick Selection Guide
For engineers evaluating these accelerators, the following quick guide summarizes typical use cases.
| Use Case | Recommended Accelerators |
|---|---|
| Multi-camera vision analytics | Hailo H8, DeepX DX-M1 |
| Ultra-low power edge devices | Hailo H8 |
| CNN pipeline workloads | MemryX MX3 |
| Mixed AI workloads | Kinara Ara-2 |
| Edge LLM inference (≤3B models) | RK1820 |
| Edge LLM inference (≤7B models) | RK1828, Kinara Ara-2 |
Key Takeaways
- Edge AI accelerators are increasingly specialized.
Vision-focused NPUs such as Hailo H8, DeepX DX-M1, and MemryX MX3 prioritize high-FPS CNN inference. - Support for transformer models is emerging in newer edge processors.
Chips such as RK1820, RK1828, and Kinara Ara-2 target local LLM inference. - Memory architecture plays a critical role in LLM workloads.
Stacked DRAM and larger onboard memory can significantly reduce memory bottlenecks. - Power efficiency remains a key constraint in edge deployments.
Many embedded systems must operate within strict thermal and power limits.