Local AI Hardware.
Built for AI.

Local AI hardware is a dedicated device that runs AI models (LLMs, speech recognition, image processing) entirely on your own hardware — no cloud, no subscriptions, no internet required for AI inference. ClawBox is purpose-built local AI hardware with a 67 TOPS NVIDIA Jetson Orin Nano that runs models like Llama 3.1 8B at 15 tokens/second.

ClawBox runs Llama 3.1 8B (~15 tok/s), Mistral 7B (~4 tok/s), Gemma 2B (~9 tok/s), Phi-3 Mini (~7 tok/s), Whisper STT (90+ languages, local), and Kokoro TTS. Models up to 13B parameters fit on the 8GB unified memory. Larger models can be quantized to fit. Managed via Ollama, the de-facto local AI model manager.

A GPU desktop (e.g., RTX 4090) runs AI much faster, but costs €1,500-3,000+, draws 300-500W, and isn't designed for 24/7 always-on use as an AI assistant. ClawBox draws only 15W, costs €549, and is purpose-built for AI assistant workloads — with messaging integrations, browser automation, voice processing, and proactive monitoring built in. It's not about raw speed; it's about having an AI that's always on, always available, always private.

With ClawBox, no. Setup is 5 minutes: plug in the power, connect to WiFi, scan the QR code to link Telegram or WhatsApp. OpenClaw (pre-installed) handles all AI model management, voice processing, and integrations automatically. You don't need to install drivers, compile models, or configure software. For advanced customization, full SSH/root access is available.

To run local AI effectively, you need at least 8GB of unified or GPU memory, a modern CPU or GPU/NPU with AI acceleration, and an always-on device. ClawBox exceeds all these: 8GB LPDDR5 unified memory, 67 TOPS NVIDIA GPU, 1024 CUDA cores, and 512GB NVMe for model storage. See our full hardware requirements at openclawhardware.dev/hardware-requirements.