Large language models (LLMs) are impressive, but their massive size makes them expensive and difficult to deploy. What if we could shrink these AI giants while keeping their smarts? NVIDIA's new Puzzle framework does just that. Imagine building with LEGOs: Puzzle breaks down a pre-trained LLM into smaller “blocks,” then swaps these with lighter, faster alternatives, like swapping a bulky LEGO piece for a smaller, sleeker one. This “blockwise local distillation” process trains each replacement independently, making it drastically faster than retraining an entire model. Then, using a clever algorithm inspired by the classic Knapsack Problem, Puzzle reassembles the best-performing blocks into a smaller, optimized model. The result? A model tailored for specific hardware, like NVIDIA's powerful H100 GPUs, that achieves incredible speedups. Take Nemotron-51B, built from the massive Llama-3.1-70B-Instruct model. Thanks to Puzzle, Nemotron-51B runs over twice as fast on a single H100 GPU while retaining nearly all of its parent's accuracy. This breakthrough has significant implications for deploying LLMs in real-world applications. Instead of needing massive clusters of GPUs, powerful AI could run efficiently on single devices, making them more accessible and affordable. Puzzle isn’t just about shrinking existing LLMs; it’s about building them smarter from the start. By understanding which model components are crucial for performance, future LLMs can be designed with efficiency in mind. This exciting development opens doors for faster, cheaper, and more accessible AI across various applications, from chatbots to scientific research. Challenges remain, like ensuring seamless integration between these rearranged components. However, NVIDIA's Puzzle framework marks a significant leap towards a future where cutting-edge AI is within everyone's reach.