作者： Jakub Rusinowski · 最后更新： 2026年7月10日
There are two ways to put AI on your phone. On-device inference runs a small 1B–4B model directly on the phone's chip — fully offline, but limited (see [Running LLMs on Your Phone](/guides/running-llm
There are two ways to put AI on your phone. On-device inference runs a small 1B–4B model directly on the phone's chip — fully offline, but limited (see Running LLMs on Your Phone). Remote inference runs the model on your powerful desktop and streams the chat to your phone — so you can drive a 14B, 32B, or even 70B model from your pocket.
This guide covers the remote path with LM Studio's LM Link and the Locally app, plus a manual fallback for Android and advanced users.
LM Studio splits this into two pieces:
| Piece | What it is | Where |
|---|---|---|
| Locally | The chat app | iPhone / iPad (App Store) |
| LM Link | The encrypted remote-access layer | Built into LM Studio + Locally |
LM Link runs on top of a custom Tailscale mesh VPN. Both devices sign into the same LM Studio account, and that shared identity authenticates the link. Only a device-discovery list ever touches LM Studio's servers — your chats stay on your devices.
> Status (June 2026): Requires LM Studio 0.4.16+. The early request-gated Preview waitlist was removed on June 8, 2026, so LM Link is open to everyone. It is free during the Preview; Locally is iPhone/iPad only at launch (no Android yet).
Step 1 — Prepare the desktop 1. Update LM Studio to 0.4.16+. 2. Sign in to your LM Studio account. 3. Download a model to serve. From the app press Cmd/Ctrl + Shift + M to search models, or use the CLI: ``bash lms get lmstudio-community/Qwen2.5-7B-Instruct-GGUF ``
Step 2 — Enable LM Link 1. Open the LM Link page inside LM Studio. 2. Turn it on (no waitlist required since the June 8 build).
Step 3 — Set up your phone 1. Install Locally from the App Store. 2. Sign in with the same LM Studio account. 3. Your desktop appears as an available host — select it.
Step 4 — Chat 1. Pick a model your desktop has loaded. 2. Send a message. Inference runs on the desktop; the response streams to your phone over the encrypted link.
That's the whole setup. No IP addresses, no port forwarding, no QR codes — the shared account login establishes the mesh automatically.
Locally is iOS-only for now, but any OpenAI-compatible chat app can reach LM Studio's built-in server.
Step 1 — Start the LM Studio server
``bash lms server start --port 1234 ``
http://localhost:1234/v1 with endpoints like /v1/chat/completions, /v1/models, and /v1/embeddings.Step 2 — Make it reachable from your phone
0.0.0.0 instead of 127.0.0.1).192.168.1.42).Step 3 — Point a mobile client at it
http://<desktop-LAN-IP>:1234/v1 and leave the API key blank (or any placeholder).Step 4 (recommended) — Secure remote access with Tailscale
100.x.y.z) instead of the LAN IP.| Desktop VRAM / Unified RAM | Good remote model | Why |
|---|---|---|
| 8 GB | Llama 3.1 8B / Qwen2.5 7B (Q4) | Fast, fits comfortably |
| 12–16 GB | Qwen2.5 14B / Mistral Small (Q4) | Big quality jump, still snappy |
| 24 GB | Qwen 3 32B / DeepSeek R1 32B (Q4) | Desktop-class reasoning on your phone |
| 32 GB+ / big Mac | Llama 3.3 70B (Q4) | Frontier-class local intelligence remotely |
Use the Hardware Analyzer to confirm fit, and browse the Model Library — each model page has a one-click lms get command and an LM Link shortcut.
127.0.0.1) is what protects you. The moment you bind to 0.0.0.0, anyone on the network can hit the API — secure it at the network level or front it with an authenticating reverse proxy, and prefer Tailscale over port-forwarding for remote access.1234.> Remember: LM Link is *remote*, not *offline*. It needs a connection between phone and host. For true airplane-mode AI, use on-device apps — see Running LLMs on Your Phone.
→ Read the deep-dive blog post | → Compare Ollama vs LM Studio vs Jan | → Set up a Local API Server