This post details CoreWeave's infrastructure for training, fine-tuning, and serving Large Language Models (LLMs). It highlights the use of bare-metal hardware, NVIDIA Quantum InfiniBand networking with NVSWITCH and NVIDIA SHARP for distributed training, and NVLINK for fine-tuning. For serving, it mentions KNative Serving on Kubernetes for auto-scaling and fast spin-up times, and provides a comparison table of various LLMs (e.g., BLOOM 176B, GPT-J 6B) based on parameters, context window, and use cases. It also emphasizes cost-effectiveness and performance-adjusted costs.