mmgp
Memory Management for the GPU Poor
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10; models must be loaded to CPU device first; minimum 6 GB VRAM and 24 GB RAM; Windows requires an extra 16 GB RAM vs.
- Linux.
- Low install friction; pure Python wheel with five runtime dependencies (torch, optimum-quanto, accelerate, safetensors, psutil).
License · maintenance · safety
(unclear) — License status is unclear—no SPDX identifier or raw license text provided. Verify licensing terms before use in proprietary or commercial projects.
last release 2026-08-06 (8 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 146,239 downloads/mo, #11,101 on PyPI
Alternatives
Verify before relying
pip install mmgp
from mmgp import offload, profile_type
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to("cpu")
offload.profile(pipe, profile_type.HighRAM_LowVRAM_Fast)- Actual compatibility with PyTorch versions and specific model architectures beyond those listed in examples.
- Performance benchmarks comparing the five profiles under identical hardware/model conditions.
- Whether safetensors library rewrite maintains full API compatibility with standard safetensors.
- Stability and edge cases when switching profiles mid-pipeline or with custom model architectures.
What it is and 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.
Typically 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
mmgp on PyPI
Before you install
Low install friction; pure Python wheel with five runtime dependencies (torch, optimum-quanto, accelerate, safetensors, psutil). Active maintenance with release 8 days old.
Requires Python >=3.10; models must be loaded to CPU device first; minimum 6 GB VRAM and 24 GB RAM; Windows requires an extra 16 GB RAM vs. Linux.
License in practice
License status is unclear—no SPDX identifier or raw license text provided. Verify licensing terms before use in proprietary or commercial projects.
Quickstart
pip install mmgp
from mmgp import offload, profile_type
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to("cpu")
offload.profile(pipe, profile_type.HighRAM_LowVRAM_Fast)Verify before relying
- Actual compatibility with PyTorch versions and specific model architectures beyond those listed in examples.
- Performance benchmarks comparing the five profiles under identical hardware/model conditions.
- Whether safetensors library rewrite maintains full API compatibility with standard safetensors.
- Stability and edge cases when switching profiles mid-pipeline or with custom model architectures.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagestorchoptimum-quantoacceleratesafetensorspsutil |
| Maintenance | Actively maintained 8 days since the last release |
| First released | |
| Downloads | 146,239 / month, #11,101 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: mmgp-3.7.12-py3-none-any.whl
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See also comfy-aimdo · smmap2 · smmap · nvidia-modelopt · auto-gptq · humming-kernels · fastsafetensors · cache-dit · ps-mem · flashinfer-python