In NVIDIA Jetson Orin Nano Super on Turing Pi 2.5: Complete Setup Guide, we moved an 8GB Jetson Orin Nano module from NVIDIA’s developer kit to Turing Pi 2.5, flashed Jetson Linux directly through the board, booted from NVMe, connected over Ethernet and SSH, and verified JetPack and CUDA. Once that node is running, the […]
If you already have a Jetson Orin Nano Developer Kit, have a spare Jetson module sitting around, or are buying a standalone module that needs a board to run on, Turing Pi 2.5 gives it a very different home from NVIDIA’s usual developer-kit carrier. The Jetson can be installed directly as one of the Turing […]
The RK3588 is often introduced as a list of specifications: eight CPU cores, a Mali-G610 GPU, a 6 TOPS NPU, 8K-class video support, and high-speed I/O. Those specifications are useful, but they do not explain how the chip behaves under real workloads. An application does not simply “run on the RK3588.” Latency-sensitive work may run […]
Once your code is already living in Gitea, the next repetitive step is usually obvious: every push still depends on someone remembering to run the tests, build the project, and check that nothing broke. That works for a personal repository until it does not. A small change skips a local test. A pull request builds […]
Passwords are among the most sensitive services you can move into a homelab. A password manager must remain available, synchronize reliably across devices, use HTTPS correctly, and have backups that can actually be restored. Vaultwarden provides a lightweight, unofficial implementation of the Bitwarden client API. It works with the official Bitwarden browser extensions, mobile apps, […]
Small language models are reaching the point where running one locally on an ARM board no longer feels like a proof of concept. Liquid AI’s LFM2.5-2.6B is built for efficient on-device inference, and the official GGUF release means it can run directly through llama.cpp without a separate model-conversion workflow. In this guide, we run the […]
The RK3588 inside the Turing Pi RK1 includes a dedicated neural processing unit rated for up to 6 TOPS, but most local LLM workloads on the platform never use it. Ollama and standard llama.cpp builds run language models on the RK3588 CPU. That path is mature, supports a wide range of GGUF models, and requires […]
If you’ve already deployed Ollama or llama.cpp on an RK1 node, getting an LLM running is no longer the challenge. The harder question is whether you’re running it efficiently. Should you choose Q4_K_M or Q8_0? How much generation speed do you lose as model size increases? And how much memory does each quantization actually consume […]
If you’ve followed this series, you’re probably typing IP addresses and port numbers into your browser more often than you’d like. Every self-hosted service has its own address, whether that’s Gitea, Nextcloud, Immich, Home Assistant, or Jellyfin. As your homelab grows, those addresses become harder to remember, bookmark, and share. A reverse proxy fixes that […]
If you followed the Pi-hole and Tailscale on Turing Pi article, you already have a private mesh between your own devices. Install it on your phone, laptop, and Turing Pi node, and they can securely communicate as though they’re on the same LAN, no matter where you are. If you also worked through the Cloudflare […]