{
  "id": 17207860,
  "title": "How to Keep Several Local AI Models Loaded at Once on Linux in 2026 (No Reload Wait)",
  "url": "https://urgent.news/2026/10/30/how-to-keep-several-local-ai-models-loaded-at-once-on-linux-in-2026",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-30T09:54:17.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/alichherawalla/how-to-keep-several-local-ai-models-loaded-at-once-on-linux-in-2026-no-reload-wait-27lc"
  },
  "original_language": "en",
  "account": "Keeping multiple AI models running simultaneously on a Linux system can be a challenge, especially when each model reload takes time. Off Grid AI Desktop for Linux addresses this issue by allowing several chat models to remain in memory at once. When you request one of these models, it is already loaded and ready to use. Before starting the loading process, the app assesses your GPU's VRAM, RAM, and CPU resources to determine if the requested models can fit.\n\nTo use this feature, you need a 64-bit x86 Linux machine running Ubuntu 24.04 or later, along with the appropriate version of Off Grid AI Desktop (0.0.55 or later) and two or more chat models downloaded. The app supports both text and vision models for GPU speed optimization, particularly for NVIDIA cards. AMD or Intel GPUs are supported as well, with no additional requirements.\n\nThe app uses a single llama.cpp model router and selects the fastest engine available: CUDA, Vulkan, or CPU. It reads the available memory from the engine, considering the VRAM of discrete cards separately from system RAM. Each kept model requires its model file, vision file (if applicable), context cache, and a 1.5 GB working buffer. The app also allocates 16,384 tokens of context for each model. If the total memory needed exceeds the available VRAM, some models will be moved into system RAM based on their architecture.\n\nWhen the app runs out of GPU space, it shifts the memory usage to system RAM, prioritizing the expert layers of mixture-of-experts models and whole layers of dense models. If no usable GPU is found, all models are loaded into RAM. Each kept model receives an equal share of CPU threads, with a maximum of 8 threads per model. The app respects CPU limits set by cgroup quotas, even within Docker containers or rented GPU pods.\n\nTo utilize this feature, open Off Grid AI Desktop, navigate to the Gateway section, and scroll to the Keep loaded section. Here, you will find checkboxes for each downloaded chat model. Select the models you want to load, and the app will display the memory usage for each model, as well as the total VRAM and RAM required. If the set exceeds your system's limits, the app will highlight the issue and allow you to remove models to fit within the available resources. Once you have adjusted the selections, clicking the Keep loaded button will initiate the loading process. Each model will display a loading badge and change to \"loaded\" once it is ready. You can call kept models using their names or IDs with any OpenAI-compatible client.",
  "summary": "Your editor plugin talks to a small coding model. Your chat client talks to a bigger one. A script on the same box wants a third. Every switch means one model unloads, the next loads from disk, and you wait. Off Grid AI Desktop on Linux keeps several chat models in memory at the same time. A request that names one of them goes straight to it, already loaded. Before anything loads, the app sizes…",
  "key_points": [
    "Off Grid AI Desktop allows multiple AI models to remain loaded simultaneously on Linux.",
    "App assesses GPU, RAM, and CPU resources before loading requested models.",
    "Users can select models to keep loaded in the Gateway section of the app."
  ],
  "editors_take": "By managing memory allocation across VRAM and system RAM, this software eliminates model reload delays to transform local Linux machines into responsive, multi-model AI workstations.",
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}