To run a local language model without a desktop window, pick one of three server-oriented routes: LM Studio’s headless daemon (llmster), llama.cpp’s llama-server binary, or Ollama running as a background service. Each one keeps an HTTP API alive after you log out of the GUI or never open it at all. By default, all three listen only on the local machine. Opening the server to other devices is a separate, deliberate step that needs authentication and firewall rules, and this guide covers that step after the setup itself.
Choose a runtime first
The runtime determines how the service starts, which API paths clients use, and how much control you have over the process. The table compares the documented values. Where a value does not appear in the vendor documentation reviewed for this guide (October 2026), the cell says so rather than guessing.
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| Runtime | Start command or unit | Default listener | Local API base | Documented GPU path |
|---|---|---|---|---|
| LM Studio llmster | lms daemon up, then lms server start |
Not stated in the headless guide; the network guide treats 127.0.0.1 as the local-only bind |
Not stated in the headless guide reviewed; OpenAI-compatible paths are referenced | Described as suitable for GPU rigs; no specific GPU requirement stated |
llama.cpp llama-server |
./llama-server -m <model.gguf> -c 2048 |
127.0.0.1:8080 |
http://127.0.0.1:8080; native /completion; OpenAI-compatible /v1/completions |
CUDA server container run with --gpus all and GPU layers |
| Ollama | systemctl service on Linux (installed as a systemd unit) |
127.0.0.1:11434 |
http://localhost:11434/api (native) and http://localhost:11434/v1 (OpenAI-compatible) |
Placement shown by ollama ps (CPU, GPU, or mixed) |
If you want the smallest moving parts and a service-first design, llama.cpp is the most direct. If you already use Ollama’s model library, its systemd service is the least work on Linux. LM Studio’s llmster suits machines where you want the LM Studio model manager and its own API without the desktop application.
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LM Studio describes llmster as a standalone daemon that runs independently of the desktop GUI. Its headless guide notes that this is different from running the desktop app in a background mode, which only makes sense where the app and a graphical environment already exist. Use llmster on Linux servers, cloud hosts, and machines with no display.
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Install and start the daemon
- Install on Linux or macOS with the command from LM Studio’s headless documentation:
curl -fsSL https://lmstudio.ai/install.sh | bash - Start the daemon:
lms daemon up - Start the API server:
lms server start
Understand just-in-time loading
The headless guide describes Just-In-Time (JIT) model loading. With JIT on, an inference request can load a downloaded model into memory when it is needed. With JIT off, you must load the model first. JIT-loaded models unload automatically after a configured period of inactivity. Treat this as a memory-management convenience. It does not guarantee a fast first response, because the first request may wait for the model to load.
Start on boot
The headless guide links to a separate Linux startup-task guide that configures startup through the system service manager. Follow that guide for boot-time startup rather than adapting the commands above yourself, because the service definition depends on your distribution and the user the daemon runs as.
Option 2: llama.cpp llama-server
The llama.cpp server README gives the plainest path: a single binary, a model file in GGUF format, and an HTTP listener.
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Run the server
- Build or install
llama-serverand place a GGUF model where you can reference it. - Start the server:
./llama-server -m models/7B/ggml-model.gguf -c 2048The README’s Unix quick start uses that model path and a 2048-token context (
-c 2048). Substitute your own model file. - The default listener is
127.0.0.1:8080, which keeps the example reachable only from the same host.
Check readiness
Loading a large model takes time, so check GET /health before sending requests:
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curl http://127.0.0.1:8080/health- HTTP 503: the model is still loading.
- HTTP 200 with
{"status":"ok"}: the server is ready.
Choose the right endpoint
/completion is llama.cpp’s own completion endpoint, and the README’s curl example uses it. OpenAI-compatible clients should use /v1/completions instead. Configure your client with the path it expects, not the native one.
Use the Docker and CUDA examples
The README documents a Docker server image and a CUDA variant. The CUDA example passes the GPU into the container with --gpus all and sets GPU layers as shown in the README. Those are configuration illustrations for supported setups. They are not a benchmark or a minimum hardware specification.
Run it as a systemd service on Linux
llama.cpp does not ship a unit file, so write one. A minimal example, with the paths and user adjusted to your machine:
[Unit]
Description=llama.cpp server
After=network-online.target
[Service]
User=llm
ExecStart=/opt/llama.cpp/build/bin/llama-server -m /srv/models/model.gguf -c 2048
Restart=on-failure
[Install]
WantedBy=multi-user.targetSave it as /etc/systemd/system/llama-server.service, then run sudo systemctl daemon-reload, sudo systemctl enable --now llama-server, and systemctl status llama-server to confirm it is active.
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Option 3: Ollama
Ollama’s FAQ says the server binds to 127.0.0.1:11434 by default. On Linux, an installation that uses a systemd service is managed with the usual systemctl commands.
Check that the service is running and enabled
- Confirm the service is active:
systemctl status ollama - Confirm it starts at boot:
systemctl is-enabled ollamaIf the answer is not
enabled, runsudo systemctl enable ollama.
Change the bind address
Ollama reads its bind address from the OLLAMA_HOST environment variable. For a systemd install, the FAQ’s method is to open an override file, add the variable under [Service], reload systemd, and restart the service:
- Open the override editor:
sudo systemctl edit ollama.service - Add the variable under the
[Service]section, for example:[Service] Environment="OLLAMA_HOST=127.0.0.1:11434"Replace the address only when you intend to change reachability, as explained in the network section below.
- Reload systemd and restart Ollama:
sudo systemctl daemon-reload sudo systemctl restart ollama
Confirm model placement and context
Run ollama ps after loading a model to see whether it runs on CPU, GPU, or a mix. Ollama’s FAQ gives a default context window of 4096 tokens. That is a software default, not a requirement of any model, and it can be overridden in the server or model settings.
Use the API base URLs
Ollama’s API introduction lists http://localhost:11434/api for its native API and http://localhost:11434/v1 for its OpenAI-compatible API. Local requests do not need an API key. Those rules apply to the local server only. Ollama’s cloud service is documented separately, and its authentication behavior should not be assumed for a local install.
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Reaching the server from another device
A server bound to localhost answers only clients on the same machine. To serve another device, the runtime must bind to an address the local network can reach, and your firewall must allow the traffic. Those two steps change who can reach the service, so they belong together.
- LM Studio: the network documentation’s CLI example is
lms server start --bind 0.0.0.0. The documentation states: “Any bind other than127.0.0.1exposes the server beyondlocalhost; we recommend enabling authentication.” Enable authentication in LM Studio before you bind beyond localhost. - llama.cpp: set the listen address with the host option available in your build (confirm the flag with
llama-server --help). The README says that public deployment should use an API key and a reverse proxy. For a local network, it recommends setting CORS to the origin of your frontend. - Ollama: change
OLLAMA_HOSTas shown above. The FAQ explains how to change reachability but does not describe authentication for an exposed bind. Keep it on localhost, or place an authenticating reverse proxy in front of it.
Limit the firewall to your local subnet
Open the port only for the network you control. On a host using ufw, for a 192.168.1.0/24 home network and Ollama’s default port:
sudo ufw allow from 192.168.1.0/24 to any port 11434 proto tcpDo not forward the port on your router to the internet.
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CORS settings control which browser origins may call the API from a web page. They do not stop a script, a command-line client, or another server from making requests. Use CORS together with an API key or proxy-level authentication and with network restrictions.
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Test from a second device
From another machine on the same network, replace the address with your server’s LAN IP and run:
curl http://192.168.1.50:8080/healthA 200 response with {"status":"ok"} confirms that the bind address and firewall both allow the connection. If the request is refused, check the bind address first, then the firewall.
Hardware and model fit
The vendor documentation reviewed for this guide does not set one minimum memory or GPU requirement for local models. Hosts and accelerators vary, and each model has its own requirements. Check the model’s documentation for memory needs, then load it on the target machine and read the placement report. With Ollama, use ollama ps. With llama.cpp, the /health response tells you when loading has finished, but it does not report speed. Measure response times yourself, because no throughput figure in this guide applies to your hardware.
Quick Recap
Common failures
- Health check returns 503: the llama.cpp model is still loading. Wait and check again.
- Works on the host but not from another device: the server is still bound to
127.0.0.1, or the firewall blocks the port. - Setting changes have no effect on Ollama: the override was not reloaded. Run
sudo systemctl daemon-reloadand thensudo systemctl restart ollama. - First request is slow in LM Studio: JIT loading may be loading the model on demand. Load the model explicitly before serving traffic if you need consistent first responses.
- OpenAI client gets 404 from llama.cpp: the client is calling
/completion. Point it at/v1/completions.
Checklist before you go live
- The service starts at boot, and you have confirmed it with a restart.
- The API base URL and endpoint paths match what your client expects.
- The bind address is
127.0.0.1unless you deliberately need LAN access. - Authentication, a reverse proxy, or both are in place before any non-localhost bind.
- The firewall allows only your local subnet.
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