Turn your Mac Studio into a team model endpoint

Your Mac Studio’s unified memory can hold models a laptop can’t, which is why it ends up running LM Studio, Ollama, MLX Studio, or MLX-LM Server as a headless inference box. The part that isn’t solved for you is reaching that model from anywhere other than the Mac Studio itself. Run the Tokios connector next to your runtime and you get one authenticated endpoint at https://api.tokios.com — no binding the server to 0.0.0.0, no VPN, no per-teammate network setup.

LAN is not the same as your team

LM Studio’s own docs cover serve on local network — binding the server beyond 127.0.0.1 so other devices on your Wi-Fi can reach it — with a warning attached, and its request-level auth is off by default. That gets a second machine on your home network talking to the Mac Studio. It doesn’t get you:

A teammate who isn’t on your LAN. Someone working from another building, another city, or their own home network can’t reach a server bound to your local subnet.
Per-person access control. LM Studio’s network server has no built-in API key, so anyone who can reach the bound port and path can call it — there’s no way to hand one teammate access and not another, or to cut someone off later without restarting the server.
One address that survives you changing machines. A LAN IP is tied to your network. Move the Mac Studio, change routers, or hand the job to a Mac mini instead, and every client’s config breaks.

Tokios doesn’t change how LM Studio, Ollama, MLX Studio, or MLX-LM Server runs — it adds a connector that dials out from the Mac Studio to https://api.tokios.com, so you never bind the runtime past loopback and never open an inbound port.

Want a GUI without the terminal? MLX Studio is a native macOS app that bundles the vMLX inference engine, a model manager, and an OpenAI-compatible API server — no pip, no venv, no command line. Download the app, pick a model from the mlx-community on Hugging Face, and start the server from the Server tab. It listens on port 8000 by default.

Mac Studio configurations and what they can run

The current Mac Studio ships with M4 Ultra, M5 Max, or M5 Ultra chips. Here is what each brings to local inference:

Chip	Unified memory	Memory bandwidth
M4 Ultra	up to 192 GB	~820 GB/s
M5 Max	up to 128 GB	460–614 GB/s
M5 Ultra	up to 256–512 GB	1,200 GB/s (50% above M3 Ultra)

With that much unified memory, a Mac Studio handles model sizes that need a multi-GPU server on other platforms:

70B models at Q4 or Q8 quantization run comfortably on M4 Ultra (192 GB) and every M5 config.
500B+ MoE models like DeepSeek V4.1-Flash (552B) and Kimi K3 (2.8T at lower quant) need multi-device setups — see DeepSeek V4.1-Flash local hardware.
DeepSeek V4-Flash at native precision (155 GB) fits the M5 Ultra 256 GB config with room for KV cache.
Multiple smaller models simultaneously — run a coding model on Ollama and a general-purpose model on MLX Studio at the same time, each behind its own Tokios deployment.

MLX-LM Server and MLX Studio are optimized for Apple Silicon. The M5 Ultra’s Neural Accelerators deliver up to 4x faster prompt processing compared to previous generations, so MLX-based runtimes get the largest gains on M5 hardware.

Current Mac Studio pricing starts at $2,499 for the M5 Max and $5,499 for the M5 Ultra.

Set it up on your Mac Studio

Start your runtime on the Mac Studio

Run the model server as you normally would, on loopback.

# Developer (Local Server) tab → Start Server
# Listens on http://127.0.0.1:1234/v1 by default

ollama run gemma4:e4b
# Listens on http://127.0.0.1:11434/v1

pip install mlx-lm
mlx_lm.server --model mlx-community/Qwen-3.5-4B-8bit
# Listens on http://127.0.0.1:8080/v1

# Server tab → Start Server
# Listens on http://127.0.0.1:8000/v1 by default

Confirm it answers on its own OpenAI-compatible /v1 API before adding the connector.

Get a claim code and pair the connector

In the dashboard, open the Setup tab and select Start pairing to get a one-time claim code shaped TKS-XXXX-XXXX (valid about 15 minutes). On the Mac Studio, run:

curl -fsSL https://tokios.com/downloads/install/macos | sh -s -- --code TKS-XXXX-XXXX

Tokios ships a native Apple Silicon (arm64) build of tokios-connector, which covers Mac Studio, Mac mini, and MacBook Pro on Apple Silicon. If Gatekeeper flags the binary, clear it with xattr -d com.apple.quarantine ./tokios-connector; the install one-liner above avoids this.Back in the dashboard, approve the device on the Setup or Connectors tab. The connector’s token is delivered over the tunnel — you never see it on screen or paste it into a file.

Point the connector at your runtime's /v1 endpoint

The connector’s upstream is your runtime’s own OpenAI-compatible API on the Mac Studio, still on loopback — not a port you expose to the network. Default ports: LM Studio 1234, Ollama 11434, MLX Studio 8000, MLX-LM Server 8080 (all with /v1 appended). No inbound firewall rule, no LAN binding, and no extra connector settings needed for this single-machine path.

On the Models tab, map your upstream model id (for example, the LM Studio or Ollama model you loaded) to a public deployment name such as studio-model. Clients send this deployment name in the model field, not the upstream id.

Create an API key

On the Keys tab, create a key. Keys are sk-tok-… Bearer tokens, shown once, and can be scoped to specific deployment name patterns — so a teammate’s key can be limited to just this deployment.

Keep it always on

A terminal window works for testing, but a Mac Studio doing this job full time should keep the connector running across reboots and logouts. Run it as a launchd background service using a ~/Library/LaunchAgents/com.tokios.connector.plist agent that points ProgramArguments at tokios-connector --config and your tokios-connector.json, with RunAtLoad and KeepAlive set so it restarts automatically. See Connector config: running as a background service for the full plist and launchctl load / launchctl start commands. For a machine no one ever logs into interactively, consider a LaunchDaemon instead of a LaunchAgent — check Apple’s current guidance for your macOS version to confirm which is preferable for your setup.

Use it from anywhere

Once the connector is paired and a deployment is registered, point your client at Tokios instead of the Mac Studio’s IP address or hostname.

export ANTHROPIC_BASE_URL=https://api.tokios.com
export ANTHROPIC_API_KEY=sk-tok-YOUR_KEY
claude --model studio-model

from openai import OpenAI

client = OpenAI(
    base_url="https://api.tokios.com/v1",
    api_key="sk-tok-YOUR_KEY",
)

response = client.chat.completions.create(
    model="studio-model",
    messages=[{"role": "user", "content": "Hello from the team"}],
)

Claude Code’s model picker only lists claude* ids, so your Tokios deployment won’t appear in the list — pass --model studio-model explicitly.

If you run more than one model on the Mac Studio — say LM Studio for one model, Ollama for another, MLX Studio for a third, and MLX-LM Server for a fourth — use the connector config file to define multiple routes and upstreams instead of the single-model pairing flow. See Model routing for registering and switching between several deployments behind the same endpoint.

Why one endpoint

Share the Mac Studio without sharing your network. A teammate calls https://api.tokios.com with their own scoped key — they never need to be on your LAN or Tailscale tailnet.
Swap models without touching client config. Point studio-model at a different upstream model or backend, and every agent and script that already targets it keeps working.
No inbound ports, no 0.0.0.0. LM Studio, Ollama, MLX Studio, and MLX-LM Server stay on 127.0.0.1. The connector only makes outbound connections to Tokios.

Next steps

Quickstart

Go from zero to a working connector, deployment, and API key.

Install the connector

Full connector install and pairing reference for all platforms.

Use Claude Code

Configure ANTHROPIC_BASE_URL and ANTHROPIC_API_KEY in detail.

Connector config reference

Serve multiple models from one Mac with tokios-connector.json, and run it as a launchd service.

Hardware & fit

Serve & connect your runtime

Connect a client or app

Compare

Turn your Mac Studio into a team model endpoint

LAN is not the same as your team

Mac Studio configurations and what they can run

Set it up on your Mac Studio

Keep it always on

Use it from anywhere

Why one endpoint

Next steps

Quickstart

Install the connector

Use Claude Code

Connector config reference

​LAN is not the same as your team

​Mac Studio configurations and what they can run

​Set it up on your Mac Studio

​Keep it always on

​Use it from anywhere

​Why one endpoint

​Next steps

Quickstart

Install the connector

Use Claude Code

Connector config reference

LAN is not the same as your team

Mac Studio configurations and what they can run

Set it up on your Mac Studio

Keep it always on

Use it from anywhere

Why one endpoint

Next steps