Loading a large language model (LLM) onto GPUs like the NVIDIA A100 often reveals unexpected challenges with VRAM usage during inference, leading to memory spikes and Out of Memory (OOM) errors. This occurs because the GPU memory dynamically accommodates not only the model weights but also a growing Key-Value (KV) cache that consumes significant resources, especially with long context windows and multi-turn interactions. The text explains the difference between dedicated VRAM and shared GPU memory, emphasizing the importance of understanding GPU memory requirements for LLMs, particularly the impact of the KV cache. Various strategies, such as quantization and distributed inference techniques, are proposed to optimize memory usage, though they require complex engineering. The Bento Inference Platform offers a solution by integrating these optimizations out-of-the-box, allowing AI teams to efficiently run LLMs without the need for extensive infrastructure management.