Remote Workflows

One of mold's best deployment patterns is simple:

• run mold serve on the GPU machine
• point MOLD_HOST at it from everywhere else

That gives you local-first ergonomics with remote GPU horsepower.

Basic Laptop → GPU Server

On the GPU host:

mold serve --bind 0.0.0.0 --port 7680

From your laptop or devbox:

export MOLD_HOST=http://gpu-host:7680
mold run "a cinematic portrait"

Finding Servers on Your Network

If you don't know the GPU host's address, let mold find it. Builds with the mdns feature (release binaries and the Nix package) advertise every mold serve on the local network via mDNS/DNS-SD, and mold server discover browses for them:

mold server discover
# NAME          URL                       VERSION  AUTH  GPU
# hal9000-7680  http://192.168.1.10:7680  0.14.0   -     1xNVIDIA GeForce RTX 4090
#
# Connect: export MOLD_HOST=http://192.168.1.10:7680

Add --probe for a /health latency column, --json for machine-readable output, or --timeout-secs N to browse longer on a busy network. The desktop app shows the same list under Settings → Engine with a one-click Use button.

Advertising is on by default; a server can opt out with mold serve --no-mdns or MOLD_MDNS=0 (for example on a shared or untrusted LAN). Loopback-only binds (--bind 127.0.0.1) are never advertised.

Recommended Pattern

1. mold pull models on the GPU host, not from every client machine.
2. Keep the server running so models stay warm between requests.
3. Use mold ps to confirm the client can reach the server.
4. Set HF_TOKEN on the server if you use gated Hugging Face repos.

OpenClaw and Discord

Remote workflows pair well with both:

• OpenClaw when you want agent-driven generation
• Discord Bot when you want a chat interface

In both cases, the key variable is still MOLD_HOST.

LM Studio MCP

mold mcp starts a stdio MCP server that exposes mold generation tools to LM Studio and other MCP hosts. It talks to the normal mold serve HTTP API, so keep the server running separately.

mold serve --bind 127.0.0.1 --port 7680

In LM Studio, open the Program tab, choose Install → Edit mcp.json, and add an entry like this:

{
  "mcpServers": {
    "mold": {
      "command": "/absolute/path/to/mold",
      "args": ["mcp", "--host", "http://localhost:7680"],
      "timeout": 300000
    }
  }
}

The MCP server exposes synchronous generate_image, timeout-friendly generate_image_async / generation_status, gallery tools list_gallery / get_gallery_image, list_models, list_loras, and server_status. Generation tools accept a loras array using ids or paths returned by list_loras; object entries can omit scale to use 1.0. Use the async generation flow for cold model loads or slow generations so LM Studio does not need to keep one tool call open until the image is finished. Set MOLD_API_KEY in the MCP process environment when the mold server requires one.

Remote Pulls vs Local Pulls

Behavior depends on where the command runs:

• mold pull against a reachable server downloads onto that server
• if no server is reachable, the CLI falls back to local pulling

That distinction matters if your laptop has little disk space or no GPU.

Example Multi-Client Setup

Machine	Role
GPU host	Runs mold serve, stores model files
Laptop	Runs mold run, mold list, mold ps
Discord worker	Runs mold discord or mold serve --discord
OpenClaw host	Uses mold via MOLD_HOST

Deployment Choices

• Docker & RunPod for cloud or containerized GPUs
• NixOS for declarative infra
• Deployment Overview for the high-level options

Remote Troubleshooting

If remote generation fails:

• verify MOLD_HOST
• check firewall and bind address
• run mold ps
• hit /health directly with curl

curl http://gpu-host:7680/health
curl http://gpu-host:7680/api/status