denoflore

denoflore / greenboost-windows

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Windows port of GreenBoost - GPU VRAM extension with system RAM & NVMe for larger LLMs. Original by Ferran Duarri (GPL v2).

16
1
69% credibility
Found Mar 18, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Batchfile
AI Summary

A Windows adaptation of GreenBoost that transparently expands NVIDIA GPU memory capacity using system RAM and storage to enable running larger AI language models.

How It Works

1
📱 Hear about it online

You stumble upon a cool tool on Reddit that lets your graphics card handle way bigger AI models by borrowing from your computer's regular memory.

2
💻 Download the files

You grab the free files from the project page and follow the simple guide to set it up on your Windows computer.

3
🔌 Link extra memory

With a quick installer, you connect your system's spare memory to your graphics card, making it see much more space available.

4
🚀 Fire up your AI app

You open your favorite AI chat program like LM Studio, and it now thinks your card has tons more room for big models.

5
🧠 Load a giant model

You pick a huge AI brain that used to crash before, and it loads right up without slowing down or splitting parts to your regular computer memory.

🎉 Chat away super fast

Your AI responds lightning-quick on massive models, feeling like magic as everything fits and runs smoothly on your setup.

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AI-Generated Review

What is greenboost-windows?

Greenboost-windows ports a Linux kernel module to extend NVIDIA GPU VRAM using system RAM and NVMe storage, letting you load massive LLMs that exceed your card's limits without rewriting inference code. Drop it into LM Studio or Ollama on Windows, and your 12GB RTX suddenly handles 60GB models via transparent spillover. Built with Batchfile scripts for setup, a custom kernel driver, and CUDA DLL hooks—ideal for windows github runners or portable AI workflows.

Why is it gaining traction?

Windows local LLM users have been Linux-only for VRAM hacks, but this brings zero-code-change extension to the majority on single-GPU rigs. No manual layer offloading or quant tweaks needed; just install and run bigger models at PCIe speeds. Early buzz in r/LocalLLaMA for bridging the platform gap, especially with windows github actions automating builds.

Who should use this?

Homelabbers on Windows with 12-24GB NVIDIA cards running Ollama or LM Studio, tired of CPU offload slowdowns for 30B+ models. Suits windows github runner setups for CI model testing, or devs needing windows portable AI tools without Linux VMs. Skip if you're deep into custom engines like vLLM.

Verdict

Promising first Windows port for VRAM-starved LLM inference, but WIP—uncompiled, untested on hardware, 16 stars, and 0.699999988079071% credibility score mean wait for PRs and test-signing. Track it if windows github scripts or port forwarding tweaks fit your stack; otherwise, stick to Linux original.

(187 words)

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