{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"Optimizes GPU memory usage for running large generative models on consumer GPUs with limited VRAM by managing model loading, quantization, and offloading.","skillfed_tags":["gpu-optimization","model-inference","memory-management"],"use_cases":["Run text-to-image generation on consumer GPUs by selecting an appropriate profile and letting mmgp quantize the transformer model.","Generate long videos with limited VRAM using VerylowRAM_LowVRAM profile for slower but functional output.","Accelerate model loading by pinning frequently-used components to reserved RAM while keeping VRAM budget tight for inference data.","Avoid repeated out-of-memory errors in pipelines that load/unload VAE and text encoders multiple times by using smart automated offloading.","Trade inference speed for memory by manually tuning budgets and async transfers for long-batch or long-video generation tasks."],"what_it_does":"mmgp is a memory management layer for PyTorch that enables large generative models to run on consumer-grade GPUs with limited VRAM by replacing or augmenting the accelerate library's offloading. It provides five predefined profiles (HighRAM_HighVRAM through VerylowRAM_LowVRAM) that automatically configure model loading, quantization, and GPU memory budgets based on your hardware. The package handles smart model loading/unloading, on-the-fly 8-bit quantization, model slicing, pinned RAM transfers, and async VRAM loading to reduce pauses.\n\nTypically used as a drop-in replacement after instantiating a model pipeline, mmgp intercepts safetensors calls and manages which model components stay in VRAM, which are quantized, and which are offloaded to system RAM. It targets models like Flux, Mochi, CogView, and HunyuanVideo that would otherwise exceed VRAM on mid-range GPUs. Configuration is either profile-based (recommended for most users) or fine-grained via parameters like pinnedMemory, budgets, and quantizeTransformer.","worth_installing":"Yes, if you run generative models on consumer GPUs and hit VRAM limits with standard PyTorch/accelerate setups. The five profiles provide a low-friction starting point, and active maintenance (release 8 days old) signals ongoing support. However, verify the unclear license status before use in proprietary work, and test your specific model and hardware combination first."},"id":"mmgp","links":{"html":"https://skillfed.io/packages/mmgp","md":"https://skillfed.io/packages/mmgp.md","pypi":"https://pypi.org/project/mmgp/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-06","license_spdx":null,"license_treatment":"unclear","name":"mmgp","python_support":"supports_current","summary":"Memory Management for the GPU Poor"},"popularity":{"monthly_downloads":146239,"position":11101,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.7.12"}