Scaling large language models (LLMs) like Llama 3 leads to impressive performance gains but comes with a hefty computational price tag. Imagine training a model so large it requires thousands of GPUs and millions of dollars—not exactly accessible to everyone. But what if we could make these massive models bigger and smarter *without* the massive costs? That's the promise of 'upcycling' with Mixture-of-Experts (MoE). Instead of training a gigantic, monolithic AI model, MoE breaks it down into smaller, specialized 'experts.' Like a team of specialists tackling a complex project, each expert handles a specific type of input. This allows the model to grow in capacity and tackle more complex tasks without needing a proportionally larger amount of compute. Researchers at NVIDIA explored this concept by upcycling Llama 3, an already powerful LLM, into an MoE model. They found that by using clever training techniques like 'MoE Parallel Folding,' which strategically distributes the model across multiple GPUs, they could achieve significant performance improvements. In fact, their upcycled MoE model outperformed the original Llama 3 on standard benchmarks like MMLU, demonstrating a 2% improvement in accuracy. Even more impressive, they achieved these gains with a tiny fraction—less than 1%—of the computational resources typically needed to train such a large model from scratch. This breakthrough suggests a more sustainable path towards building even more capable AI. By upcycling existing models, researchers can leverage prior investments in training and push the boundaries of AI performance without breaking the bank. This clever approach to training AI not only makes powerful models more accessible but also opens up exciting new possibilities for pushing the boundaries of AI capabilities.