Training large language models (LLMs) is computationally intensive, often requiring clusters of powerful GPUs. Traditionally, managing these resources has been a complex and tedious process for developers, demanding careful consideration of GPU types, memory capacities, and parallel processing strategies. But what if you could train LLMs without worrying about the underlying hardware? Enter Frenzy, a groundbreaking new system designed to bring the ease of serverless computing to the complex world of heterogeneous GPU clusters. Imagine simply submitting your LLM training job and letting the system handle the rest. Frenzy makes this possible by intelligently predicting the optimal combination of GPU types and quantities needed for your specific model and then efficiently scheduling the training process across a diverse set of available GPUs. This memory-aware system analyzes your model's parameters, the desired batch size, and the available hardware to estimate the peak memory usage during training. This ensures efficient resource allocation, preventing out-of-memory errors while maximizing GPU utilization. Furthermore, Frenzy's low-overhead scheduling algorithm smartly distributes the workload across the cluster, optimizing for both individual GPU performance and inter-GPU communication. The results are impressive. In tests, Frenzy demonstrated a remarkable boost in training efficiency, reducing average job completion times by 12% to 18% compared to existing state-of-the-art methods. Its memory usage predictions proved highly accurate, exceeding 92% in various scenarios. Frenzy represents a significant leap forward in simplifying LLM training. By abstracting away the complexities of hardware management, it empowers developers to focus on what matters most: building and refining powerful language models. This serverless approach unlocks greater accessibility to LLM training, paving the way for faster innovation and broader adoption of this transformative technology. While Frenzy’s initial results are promising, future work could explore further optimizations for dynamic workload fluctuations and even more sophisticated scheduling strategies to further reduce training times and enhance resource utilization. The journey toward truly democratizing LLM training is ongoing, but Frenzy is undoubtedly a significant step in the right direction.