This guide details a systematic approach to selecting the optimal accelerator and configuring vLLM for efficient Large Language Model (LLM) serving. It emphasizes that no single setup is ideal for all LLM inference use cases. The process begins with gathering crucial information about your specific workload, including the model being used, its precision, expected request volume, and sequence lengths.Understanding workload characteristics is vital for determining VRAM requirements and the necessity of tensor parallelism. The guide outlines how to estimate minimum VRAM needed, considering model weights, activations, and the KV cache. It then discusses various GPU and TPU options available on Google Cloud, such as L4, A100, H100 GPUs, and TPU v5e and v6e.Tensor parallelism, which distributes models across multiple accelerators, is explained as a method to handle large models but can introduce communication overhead affecting latency. The guide then moves into benchmarking and tuning, using an auto_tune.sh script to test different configurations. This script automatically finds the highest stable GPU utilization and tests various sequence and batch sizes.Troubleshooting tips are provided for potential issues like CUDA out-of-memory errors or profiling hangs. The goal is to identify the best price-performance point by evaluating latency and throughput across candidate accelerators. Ultimately, the guide empowers users to make informed decisions for cost-effective and high-performing LLM deployments.