Large language models (LLMs) are impressive, but their massive size makes them difficult to run on everyday devices like phones and laptops. Imagine wanting to use an AI assistant offline or keep your conversations private—you'd need a way to shrink these huge models down to size without losing their smarts. This is where new research on LLM compression comes in. Existing methods like quantization are like creating different size versions of a model, each requiring separate storage and reloading if you want to switch between them. This can be slow and clunky. A new technique called BitStack offers a more elegant solution. It works by breaking down the LLM's internal components, its weight matrices, into smaller, manageable chunks. Think of it like organizing a giant library into individual books, each representing a tiny piece of the model's knowledge. These pieces are then ranked by importance and stored. When you want to use the LLM, BitStack intelligently loads only the most important chunks, fitting the model to the available memory on your device. Need more performance? BitStack simply loads more pieces. Memory running low? It offloads the less crucial ones. This dynamic resizing happens seamlessly, allowing the LLM to adapt to the changing memory landscape of your device in real-time. Tests on popular models like Llama 2, 3, and 3.1 show BitStack can shrink LLMs drastically, even to a 2-bit level, while keeping them surprisingly capable. While there's still room for improvement, especially in optimizing inference speed, BitStack represents a significant step towards bringing the power of LLMs to everyone, regardless of their device's limitations. This could unlock a future where powerful AI assistants are always available, offline and on-device, ready to help whenever and wherever you need them.