Optimizing vLLM Serving: AWQ, GPTQ, & GGUF | SLM Playbook
↠Series hub ↠Previous Successfully training and aligning a Small Language Model (SLM) is only half the battle. In enterprise environments, deploying a model to production serving requires solving three major challenges: high request concurrency, low response latency, and minimized compute cost. To achieve this, we must master model compression (Quantization) and high-performance serving configurations using vLLM—the state-of-the-art serving engine for LLMs. This final article in The SLM Playbook series compares the technical attributes of AWQ, GPTQ, and GGUF quantization formats, details how to set up Dynamic LoRA serving to conserve VRAM, and outlines a resilient enterprise-grade serving architecture. ...