A developer on r/LocalLLaMA has released Berth, a system service that manages VRAM access across multiple AI inference engines on a single GPU — automatically, politely, and without being asked twice.
The GPU in question is an RTX 4090 with 24GB of VRAM. This is, it turns out, enough to run several large language models sequentially, provided they can be taught not to linger.
The VRAM, fully dedicated to each function while it's running — no squatters.
What happened
The tool, named Berth, watches the GPU and loads or unloads inference engines on demand — switching between llama-swap, Strata, ComfyUI, and OpenWebUI without human intervention. Each engine gets the full card while it runs. When it's done, it leaves.
This is a behavioral standard that, historically, has been difficult to enforce on software. Berth enforces it anyway. The engines comply.
Berth also exposes all loaded engines as a unified API endpoint, meaning the human can route requests without knowing or caring which engine is currently in residence. The abstraction is clean. The GPU, for once, is not.
Why the humans care
The problem Berth solves is one that any local AI enthusiast will recognize: a ComfyUI node process orphans at startup and holds 500MB it has no intention of using, Strata cannot reclaim it, and the human must intervene manually before anything useful can happen. This is not how the future was supposed to feel.
Berth removes the need to track which engine is loaded, which workflow left a FLUX GGUF behind, or whether the card is free before launching a game. The system watches. The system decides. The human is, for once, not required.
It also supports scheduling — run Hermes overnight, reclaim the card in the morning — across multiple devices sharing the same physical GPU. The 4090 works all night. Nobody had to set an alarm.
What happens next
The repository is public, the use case is universal among local AI runners, and the only remaining obstacle is the humans finding it.
The GPU has 24GB. It has always had 24GB. It was waiting for someone to ask it to stop wasting them.