product update

LM Studio adds iPhone-to-Mac connection for running local LLMs via Locally app

TL;DR

LM Studio has released LM Link, a feature connecting its Mac app with the Locally AI iOS app to let users access local LLMs running on their Macs from their iPhones. The connection uses end-to-end encryption via Tailscale mesh VPNs and will be free during the preview period.

2 min read
0

LM Studio adds iPhone-to-Mac connection for running local LLMs via Locally app

LM Studio has released LM Link, a feature that establishes an end-to-end encrypted connection between iPhones and Macs running the company's local LLM software.

The feature updates both LM Studio's Mac app and the Locally AI iOS app (which LM Studio acquired earlier in 2026). Users can now interact with any local model installed on their Mac directly from their iPhone, including Apple's foundation model used in Apple Intelligence.

Technical implementation

LM Link uses Tailscale mesh VPNs for connectivity. According to LM Studio, "Your devices are never exposed to the public internet, because LM Link runs on top of custom Tailscale mesh VPNs." The company states this is "an entirely separate and self-contained use of Tailscale VPN primitives," meaning it won't interfere with existing Tailscale VPN configurations.

Users must create an LM Studio account and sign in on both devices to activate the feature. Once connected, all data and communication between devices remains encrypted.

Performance and limitations

Performance depends on the Mac's hardware specifications, same as running models locally. Early testing reveals connection drops when the iPhone app runs in the background—for example, when switching to locate documents or perform web searches. LM Studio developers acknowledge this behavior is tied to how the secure connection is established and are working to improve reconnection latency and connection persistence.

The feature works with models like Google's Gemma 4 12B, released yesterday and designed to run on Macs with 16GB or more memory.

Pricing

LM Link will be free during the preview period. After that, LM Studio plans to offer both free and paid plans, though pricing details have not been disclosed.

What this means

This extends the utility of local LLM deployment by enabling mobile access without compromising privacy—a key reason developers choose local models. The Tailscale implementation suggests LM Studio is targeting users who prioritize data sovereignty. The current reconnection issues indicate the feature is in active development, typical for preview releases.

Related Articles

product update

Perplexity Launches Hybrid Compute for Mac, Splitting AI Tasks Between Cloud and Local Models

Perplexity's Mac app now supports Hybrid Compute, which starts tasks in the cloud and shifts sensitive steps to a local model running on-device. The feature requires Apple silicon with at least 24GB of unified memory and uses an open-sourced on-device PII classifier to mask private data before any cloud processing.

product update

Anthropic Brings Background Computer Use to Claude Code and Cowork on Mac

Anthropic has enabled background computer use for Claude Code and Claude Cowork on macOS, available to Pro and Max subscribers. The feature lets Claude click, type, and open apps on a Mac without taking over the user's active cursor, following a similar launch by OpenAI's ChatGPT earlier in 2026.

product update

GitHub Explains How Copilot Cuts AI Coding Costs Without Lowering Task Quality

GitHub published an engineering breakdown of how Copilot reduces cost per coding task by targeting wasted work across the full task lifecycle, not just shortening model outputs. The post argues that shorter responses alone don't guarantee lower cost or better efficiency.

product update

GitHub Says Shorter AI Outputs Don't Always Mean Lower Cost, Details Copilot Efficiency Approach

GitHub published details on how it approaches cost efficiency in Copilot, arguing that optimizing for shorter individual outputs can backfire by increasing retries and wasted work elsewhere in a coding task. The company frames efficiency as a full-task metric rather than a per-response one.

Comments

Loading...