@Huahuazo: Put the complete large model into a USB drive, plug and play on any computer anytime, anywhere. This open-source project called USB-Uncensored-LLM is the realization of this idea, allowing mainstream models such as Gemma and Qwen to completely break free from the constraints of network and permissions. This project achieves full compatibility with Windows, macOS and...
Summary
USB-Uncensored-LLM is an open-source project that can load large language models like Gemma and Qwen into a USB drive, enabling plug-and-play, offline operation, cross-platform compatibility. Users can use a private AI assistant on any computer without the need for internet.
View Cached Full Text
Cached at: 07/01/26, 08:03 AM
Plug the entire large model into a USB drive and use it on any computer anytime, anywhere. The open-source project USB-Uncensored-LLM brings this vision to life, freeing mainstream models like Gemma and Qwen from the constraints of network and permissions.
This project achieves full platform compatibility across Windows, macOS, and Linux. All computations are performed locally offline, and every conversation record is properly saved. Its most ingenious design is “download once, run everywhere”—model files only need to be downloaded once on any device and can be seamlessly used across multiple operating systems without repeated downloads on each computer, thus avoiding storage waste.
With a USB drive starting at just 8GB, you can have a completely uncensored, private AI model that belongs solely to you. Whether in the office, library, or café, simply plug the USB drive into any computer, and a powerful, dedicated AI assistant will be ready for you instantly.
Similar Articles
@Chuksdakingz: you can run Large LLMS on a USB drive or local hard drive platforms: mac, windows, linux and android models: > Gemma 4 …
Guide on running large language models like Gemma 4, Qwen 3.5, and Gemma 2 on local devices via USB drive, no dependencies, only 8GB RAM required.
@ai_xiaomu: Here comes a full-featured multimodal local model that runs on a MacBook with 16GB: 1. Download LM Studio; 2. Search for Gemma 4 12B and install it; 3. Ask Codex to configure the local API parameters for you; 4. Then enjoy the freedom of tokens.
Guides users on running the Gemma 4 12B multimodal local model on a MacBook with 16GB RAM using LM Studio and Codex, enabling free token usage.
@NFTCPS: 4GB VRAM running 70B large model? It actually works! AirLLM did a clever trick — layered inference, not loading the whole model into VRAM at once, but layer by layer, compute and discard, squeezing the giant into a small GPU. The best part: 100% open source, freebie warning https://github.com/0xSo…
AirLLM is a fully open-source tool that uses layered inference (loading and releasing VRAM layer by layer) to enable 70B large language models to run on GPUs with only 4GB VRAM, without quantization, distillation, or pruning. It already supports running Llama3.1 405B on 8GB VRAM.
@mylifcc: I'm already running Gemma-4-12b on my Mac. Tech stack: llama.cpp + GGUF Q4_K_M + Metal 32K context, local OpenAI-compatible API. Measured about 36 tok/s, resident RSS about…
User shares their experience using llama.cpp with the GGUF Q4_K_M quantized version of Gemma-4-12b on a Mac, achieving local inference speed of about 36 tok/s and memory usage of about 10GB.
@MinLiBuilds: Really have to thank open-source contributors like Unsloth and SGLang. Model factories releasing weights is just the first step. These people tirelessly adapt, write kernels, reduce memory usage, improve speed, add tools, write tutorials, and even provide free compute resources. Without them, many so-called 'open-source models' are just...
This article praises open-source contributors like Unsloth and SGLang, who through optimizing tools and technologies enable ordinary people to fine-tune large language models such as Qwen3.8-27B on consumer-grade GPUs, lowering the barrier to AI development.