A working Jetson installation is not automatically a maintainable AI server. In our complete setup guide, we installed an 8GB NVIDIA Jetson Orin Nano on Turing Pi 2.5, flashed Jetson Linux to a 500GB NVMe drive, connected over Ethernet and SSH, and verified JetPack and CUDA. We then covered the Jetson modules supported by Turing […]
If you are new to NVIDIA Jetson, its software stack can look more complicated than the hardware. JetPack, Jetson Linux, CUDA, cuDNN, TensorRT, containers, frameworks, inference servers, and application code often appear in the same setup guide, even though they solve different problems. The confusion usually comes from seeing all of those names at once […]
Cloud AI made powerful models easy to access. Send a request to an API, wait for a response, and someone else’s infrastructure handles the compute. That model works well, but it is not the right architecture for every workload. Private documents, camera streams, microphone audio, local automation, repeated inference, unreliable connectivity, and latency-sensitive systems can […]
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 […]