Large language models (LLMs) like ChatGPT are revolutionizing how we interact with technology, but their immense computational demands usually relegate them to powerful servers. Imagine running these complex AI models directly on your laptop, offline and at speed. Researchers have tackled this challenge by harnessing the power of Intel's Neural Processing Unit (NPU), a specialized AI accelerator found in newer Intel laptops. The problem? LLMs, by their dynamic nature, aren't readily compatible with the NPU's static inference requirements. This research introduces NITRO (NPU Inference for Transformers Optimization), a framework that bridges this gap. NITRO cleverly restructures the LLM architecture and uses a “chunking” method to convert the model into smaller, statically shaped parts compatible with the NPU, overcoming memory limitations in the process. Initial benchmarks show promising results, with NPU inference speeds outpacing CPU performance in some cases, especially for medium-sized models. While GPUs still hold the edge in raw speed, the NPU shines in its energy efficiency, paving the way for longer battery life and truly mobile AI. However, challenges remain. Current limitations include the inability to utilize weight compression techniques like quantization on the NPU, which significantly boosts performance on CPUs and GPUs. Furthermore, certain LLM features still require CPU processing, adding overhead. Future work focuses on incorporating these operations directly within the NPU and exploring techniques like speculative decoding to further enhance performance. The research highlights the potential of NPUs to unlock local, efficient LLM execution on laptops. As NPU hardware and software mature, we can expect even faster and more power-efficient LLM inference, bringing the power of AI directly to our fingertips.