Fast, Cheap, and Accurate: Optimizing LLM Inference with vLLM and Quantization

Running large language models at scale can get expensive fast, but the right optimizations can cut latency and GPU costs dramatically.

We’ll walk through how to serve models efficiently using vLLM, an open-source, high-performance inference engine. Then and generate and test quantized models, expose them through vLLM’s OpenAI-compatible API, and tune runtime flags to balance throughput, latency, and accuracy on different GPUs.

We’ll benchmark performance live, inspect token-throughput metrics, and discuss real-world deployment trade-offs.