nexa-bridge: Rust proxy brings vLLM and Ollama out from behind NAT without port forwarding

⚡ The gist in 5 seconds - NexaEthos has published nexa-bridge — an OpenAI-compatible proxy written in Rust with an outbound tunnel for vLLM, Ollama, LM Studio, and llama.cpp. - The machine running the models stays behind NAT: no need to open ports or set up forwarding — the connection goes outward. - The proxy exposes local models as an OpenAI-compatible endpoint; installation and code are in the NexaEthos/nexa-bridge repository on GitHub. ### 🔍 What was found The NexaEthos/nexa-bridge project has appeared on GitHub — a bridge between a local machine running models and the Nexa Agora platform. As stated in the repository description, it is a Rust-based proxy with an OpenAI-compatible API and an outbound tunnel. The declared backend support includes vLLM, Ollama, LM Studio, and llama.cpp. The key feature: the machine can sit behind NAT, and no port needs to be opened — the tunnel is established from inside the network, not from outside. According to the description, the architecture works in two layers: the local inference server (any of the listed ones) stays as is, the proxy receives its requests and sends them outward through the outbound connection, while the external side sees a standard OpenAI-compatible interface. In other words, clients don't need to know anything about the specific backend or the fact that the server is hidden behind NAT — they simply use the familiar API format. No version numbers, release tags, or specific flags are mentioned in the description, so configuration details and tunnel parameters should be checked in the repository itself. ### 💡 Why it matters Self-hosting LLMs is limited less by hardware than by networking: to serve a model to external clients, you usually need port forwarding, a public IP, or at least a VPN — and on a home connection or inside a locked-down corporate perimeter, that's a separate headache. nexa-bridge solves exactly this part of the problem without touching the inference itself: vLLM, Ollama, LM Studio, and llama.cpp keep running on their own machine, and only the outbound connection goes outward. A second practical advantage is the OpenAI-compatible API binding: existing clients and SDKs don't need to be rewritten for a new format. How smoothly this works in practice is a question for the repository itself — no public tests or performance figures are claimed in the project description.