Large Language Models (LLMs) are impressive, but their massive size makes them hard to run on everyday devices. Imagine trying to fit a giant whale into your bathtub – that's essentially the challenge with LLMs. New research explores a clever trick called "Basis Sharing" to slim down these models without sacrificing too much performance. Think of it like a wardrobe shared by siblings. Instead of each having identical clothes, they share basic items (like shirts and pants) but personalize them with unique accessories (like a scarf or a cool hat). Similarly, Basis Sharing lets different layers of an LLM share core "basis vectors" while keeping individual "coefficients" to maintain their unique functions. This reduces the overall number of parameters, making the model smaller and faster. The researchers tested this technique on several LLMs, including the LLaMA family, and saw promising results, especially when targeting high compression ratios. For example, they sped up some models to process text up to 1.57 times faster than their denser counterparts. While the approach is still in its early stages, it offers a fascinating solution to the memory challenges of LLMs, potentially bringing powerful AI capabilities to devices like phones and laptops. The next step? Figuring out how best to group LLM layers to further optimize and share basis vectors, opening up even more possibilities for efficient and accessible AI.