Running OpenClaw on Jetson Orin Nano 2026 — benchmarks, setup guide, power modes, ClawBox alternative

gist_jetson_orin_nano_openclaw.md

Running OpenClaw on Jetson Orin Nano — Performance Guide 2026

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The NVIDIA Jetson Orin Nano 8GB is one of the best single-board computers for running OpenClaw locally. Here's everything you need to know.

Why Jetson Orin Nano for OpenClaw?

Raw AI Numbers

• 67 TOPS (Tera Operations Per Second) — dedicated AI accelerator
• This is GPU + DLA (Deep Learning Accelerator) combined
• Compared to Raspberry Pi 5: 13 TOPS (NPU)
• Compared to Apple M4: 38 TOPS (Neural Engine)

Practical Impact

Running Llama 3.1 8B (Q4_K_M quantization):

• Jetson Orin Nano 8GB: ~15 tokens/second
• Raspberry Pi 5 8GB: ~2 tokens/second
• Apple M2 8GB: ~20 tokens/second

15 tok/s is fast enough for real-time conversation — you don't notice the generation delay.

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Hardware Setup (Dev Kit)

If you're rolling your own (not using ClawBox):

What You Need

- Jetson Orin Nano 8GB Developer Kit: ~€299
- 512GB NVMe M.2 SSD (2242): ~€60
- 5V 4A USB-C power supply: ~€15
- (Optional) Case + fan: ~€30

Flashing Jetson Linux

1. Download NVIDIA SDK Manager
2. Flash JetPack 6.x (Ubuntu 22.04 base)
3. First boot configuration

Installing OpenClaw

# Install Node.js 20+
curl -fsSL https://deb.nodesource.com/setup_20.x | sudo -E bash -
sudo apt install -y nodejs

# Install OpenClaw
curl -fsSL https://get.openclaw.ai | bash

# Start
openclaw start

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Or: Just Buy ClawBox

The above is ~8 hours of work (flashing, configuring, troubleshooting). ClawBox ships with everything pre-installed.

DIY	ClawBox
€404 hardware	€549 complete
8+ hours setup	5 minutes
Your time (@ €30/hr = €240)	€0
Total: €644+	€549

→ ClawBox — pre-configured Jetson Orin Nano 8GB

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Jetson Orin Nano Inference Benchmarks

LLM Performance (OpenClaw local mode)

Model	Size	Tokens/sec	RAM Used
Llama 3.2 3B (Q4_K_M)	2.0GB	~28 tok/s	3.5GB
Llama 3.1 8B (Q4_K_M)	4.7GB	~15 tok/s	5.8GB
Mistral 7B (Q4_K_M)	4.1GB	~17 tok/s	5.2GB
Phi-3.5 Mini (Q4_K_M)	2.2GB	~25 tok/s	3.2GB
Gemma 2 9B (Q4_K_M)	5.5GB	~12 tok/s	6.8GB

8B models fit in 8GB unified memory at Q4 quantization

Running Via Ollama (Recommended)

# Install Ollama (Jetson ARM64 build)
curl -fsSL https://ollama.ai/install.sh | sh

# Pull a model
ollama pull llama3.2:3b

# Configure OpenClaw to use it
openclaw config set agents.defaults.model "ollama/llama3.2:3b"

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Power Optimization

Jetson Orin Nano supports multiple power modes:

# Check current mode
sudo nvpmodel -q

# 7W mode (quiet, still ~10 tok/s)
sudo nvpmodel -m 1

# 15W mode (full performance, ~15+ tok/s)
sudo nvpmodel -m 0

# Check temps
tegrastats --interval 1000

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24/7 Operation Tips

1. Cooling: The heatsink that comes with dev kit is sufficient. Add a small fan for sustained loads.
2. Storage: NVMe is essential — eMMC (built-in) is too slow for LLM weights.
3. Watchdog: Use systemd to auto-restart OpenClaw if it crashes.
4. UPS: A small UPS protects against power cuts that corrupt model files.

# systemd service for OpenClaw
sudo nano /etc/systemd/system/openclaw.service
# [Service] Restart=always RestartSec=10
sudo systemctl enable openclaw

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The ClawBox Shortcut

All of the above — properly configured, tested, optimized — comes out of the box with ClawBox:

clawbox.tech — €549, ships via DHL Express