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How to stop local AI engines from crashing each other on one GPU with Berth

Queue llama.cpp, ComfyUI and other engines on one card so none load into an out-of-memory crash.

Who it is forA developer or power user running chat, image and voice models on one NVIDIA workstation who gets out-of-memory errors when a second model starts.

The problemTwo engines load into the same card at once, one fails with an out-of-memory error, and it is unclear which process holds the memory.

The outcomeEach engine waits its turn through berthd, the holder finishes its reply or job, and `berth status` shows who holds the card and who is waiting.

What you need first

  • An NVIDIA GPU on Linux, or Windows 10/11 (experimental)
  • Go installed for `go install`, or a release binary from the Install section
  • At least one local engine, such as llama-server, ComfyUI or llama-swap

Steps

  1. go install github.com/T-Crypt/berth/cmd/berth@latest, or download a release binary (see Install).
  2. berth service install --harbor to run the daemon and the harbor as user services.
  3. berth run --as llama --priority foreground -- llama-server -m model.gguf --port 8080 to start an engine under a lease.
  4. Start a second engine the same way with berth run and a lower priority such as --priority batch. (inferred, check the README)
  5. berth status to see the holder, the waiting engine and the reason it waits.

Prompt for your agent

In the Berth repo at <path to berth>, install the berth binary and run `berth service install --harbor`, then start <engine command> with `berth run --as <name> -- <engine command>`. Expected output: `berth status` lists the engine as the card holder. Verify that a second engine started through `berth run` shows as waiting with a reason, not as a crash.

Caveat. Berth only governs engines started through `berth run`, the gateway, ComfyUI's API or a game claim, and it is alpha software at v0.3.2.

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