Imagine running massive AI models, like those powering advanced chatbots, right on your phone. It sounds impossible, right? LLMs, or large language models, are notoriously resource-intensive, often requiring powerful servers to function. Yet, new research suggests a clever way to bring these powerful AIs to consumer devices. The problem lies in the sheer size of LLMs; they are too large to fit into the memory of typical consumer devices. Each time the model generates text, it needs to access its vast knowledge base, which involves extensive data transfer and processing. Generating a single word can take seconds with current offloading techniques, making interactive use frustrating. One solution that has made waves recently is called speculative decoding. This involves using a smaller “draft” AI to predict the next words in a sentence, which a larger AI then verifies in parallel. But even this approach faces an obstacle: the larger the draft AI attempts to process at once, the fewer accurate predictions it produces. So how can we make large models faster and more efficient? Researchers propose a new method, ‘Speculative Execution’ or SpecExec. Inspired by how modern CPUs predict operations to save time, SpecExec allows the LLM to generate a “cache” of likely future words based on user input. The draft AI effectively creates a look-up table of probable next words, enabling the main AI to rapidly generate text by checking against this cache. When the cache is exhausted, the process repeats. Interestingly, research shows that LLMs, especially large ones, tend to concentrate their probability on a small set of words. If the draft AI can correctly predict these words, the efficiency of the entire system shoots up. Testing SpecExec on resource-constrained GPUs with offloading to RAM, researchers saw remarkable speed improvements. Using a popular 70B parameter LLM, they achieved speeds of 4–6 tokens per second with 4-bit quantization and 2–3 tokens per second with 16-bit quantization—a massive jump from the previous 0.2 tokens per second. These speeds are sufficient for near-interactive use, even on older consumer GPUs. This research signifies a crucial step toward truly local and private AI applications. By optimizing the way large models access and process information, methods like SpecExec can unlock powerful AI capabilities on everyday devices, changing how we interact with and use AI in our daily lives. While this work demonstrates a significant performance boost, the next challenge involves streamlining the interaction between hardware and software even further. With the ever-increasing demand for powerful yet accessible AI, approaches like SpecExec might just pave the way for a future filled with local, personalized, and highly efficient AI assistants.