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  "path": "/t/overflowml-auto-optimal-model-loading-for-any-hardware/174144#post_1",
  "publishedAt": "2026-03-10T20:13:07.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "GitHub - Khaeldur/overflowml: Run AI models larger than your GPU. Auto-detects hardware, picks optimal memory strategy. · GitHub",
    "overflowml · PyPI"
  ],
  "textContent": "Sharing a library I built to solve the “model too big for GPU” problem automatically.\n\n**Problem:** Loading large models requires knowing which combination of device_map, quantization, and offloading to use — and it varies by hardware. FP8 doesn’t work with CPU offload on Windows. INT4 needs bitsandbytes. Sequential offload and attention_slicing crash together.\n\n**Solution:**\n\n\n    import overflowml\n\n    # Detects your hardware, picks strategy, loads with optimal config\n    model, tokenizer = overflowml.load_model(\"meta-llama/Llama-3-70B\")\n\n\nUnder the hood it:\n\n  * Detects GPU type, VRAM, RAM, FP8/BF16 support\n  * Estimates model size from config (no weight download needed)\n  * Picks the best strategy: direct load, FP8, BitsAndBytes INT4/INT8, model_cpu_offload, or sequential_cpu_offload\n  * Sets up device_map, max_memory, quantization_config automatically\n  * Avoids known incompatibilities\n\n\n\nAlso works with diffusers pipelines:\n\n\n    overflowml.optimize_pipeline(pipe, model_size_gb=40)\n\n\nCLI tool included:\n\n\n    $ overflowml benchmark      # shows what models your hardware can run\n    $ overflowml plan 70        # detailed strategy for a 70GB model\n    $ overflowml detect         # show hardware capabilities\n\n\nCross-platform: NVIDIA (CUDA), Apple Silicon (MPS/MLX unified memory), AMD (ROCm planned).\n\n`pip install overflowml[transformers]`\n\nGitHub: GitHub - Khaeldur/overflowml: Run AI models larger than your GPU. Auto-detects hardware, picks optimal memory strategy. · GitHub\nPyPI: overflowml · PyPI",
  "title": "OverflowML: Auto-optimal model loading for any hardware"
}