NVIDIA Jetson vs. Hailo NPU Comparison

Source: Grok (xAI) analysis, 2026-03-29

Key Comparison Table

| Aspect | Hailo-8 (on RPi 5) | Hailo-10H (GenAI) | Jetson Orin Nano Super | Higher Jetson (Orin NX 16GB) |
|--------|--------------------|--------------------|----------------------|------------------------------|
| AI Performance | 26 TOPS (INT8); ~52 TOPS dual | 40 TOPS (INT4) / 20 TOPS (INT8) | 67 TOPS (sparse/INT8) | Up to 157 TOPS |
| Power | ~2.5W typical per chip | <5W typical | 7-25W (configurable) | 10-40W+ |
| Efficiency | ~10+ TOPS/W; 3-4x better FPS/W | Strong for LLMs/VLMs at low power | Good, but higher draw | Lower efficiency at peak |
| Memory | Host (RPi 8GB+); on-chip SRAM | Host-dependent | 8GB LPDDR5 shared | 16GB+ LPDDR5 |
| Cost | ~$70-150 accelerator + ~$80 RPi 5 | Similar low | ~$249 dev kit | $399+ for modules |
| Software | Hailo Dataflow Compiler, Ollama | Expanding GenAI support | Mature JetPack, CUDA, TensorRT | Same rich ecosystem |
| Best For | Ultra-low power, sovereign/local inference | Offline GenAI (LLMs, SD) | Balanced performance + flexibility | High-throughput robotics |

Object Detection Benchmarks (YOLOv8)

Hailo-8 on Raspberry Pi 5

YOLOv8s: ~127-150 FPS (batch=8, 640x640)

YOLOv6n: Up to ~354 FPS

YOLOv8m multi-stream (720p): 1 stream ~77 FPS; 4 streams ~17 FPS; 8 streams ~8 FPS

Power: ~2.5-5W per chip + Pi base (~10W total)

Jetson Orin Nano Super

YOLO models (TensorRT INT8): 100-150+ FPS for nano/small variants

C++ + TensorRT: Up to ~33 FPS end-to-end video (12ms latency) or ~90 FPS raw

Python/ONNX: Lower (~16 FPS) due to overhead

Power: 7-25W configurable

Takeaway: Jetson leads in peak single-stream FPS with optimization. Hailo shines in multi-stream efficiency and power/thermal.

Generative AI / LLM Benchmarks (Tokens per Second)

Hailo-8 on Pi 5 (CPU fallback for LLMs)

Small models (1-2B quantized): ~1-8 TPS depending on engine

Larger models slower/unusable for real-time

Hailo-10H (40 TOPS INT4, AI HAT+ 2)

Qwen2.5-1.5B-4int: TTFT ~320ms (vs ~2s on Pi CPU alone)

Small distilled models (DeepSeek-R1 1.5B, Llama 3.2 1B): Single-digit to low teens TPS

Power: ~3-5W chip typical

Jetson Orin Nano Super

Qwen 2.5 3B, Llama 3.2 3B: ~15-35 TPS in optimized setups

Handles SLMs + vision combos better due to CUDA flexibility

RAM (8GB shared) can constrain concurrent workloads

Power: 15W+ mode for peak

Takeaway: Jetson generally higher TPS and better ecosystem for agentic/multi-modal. Hailo-10H improves Pi for offline GenAI but small models remain "novelty-level" for complex reasoning.

Power, Cost, and Efficiency

| Setup | Total System Power | Hardware Cost | Monthly Cost |
|-------|-------------------|---------------|-------------|
| Hailo on Pi 5 | ~10-15W | ~$150-230 | Very low |
| Jetson Orin Nano Super | ~15-25W | ~$249+ | Moderate |
| BlackRoad Fleet (5x Pi + 2x Hailo) | ~50-75W total | ~$1,000-1,500 | ~$38-136/mo |

BlackRoad OS Relevance

Hailo on Raspberry Pi enables practical, sovereign, low-power local AI (vision-heavy for agents/monitoring, lighter GenAI via optimizations) without hyperscaler dependency. Multiple Pi + Hailo nodes provide distributed 52+ TOPS at modest cost, with self-healing mesh handling workloads.

Choose Hailo for cost/power-optimized sovereign local AI (BlackRoad's approach). Choose Jetson when you need maximum flexibility, CUDA ecosystem, or higher raw throughput in robotics/industrial scenarios.