Quantization

← 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. ...

1. The LLM Bottleneck: Why Are GPUs Still Idle? After finishing designing the entire Agent architecture in the previous 7 parts, it is time to push your system to Production (live running). Every startup soon realizes a bitter truth: The enemy of LLMs is not Compute Power, but Memory Bandwidth. To run the Llama-3 70B model (standard FP16), you need about 140GB of VRAM just to hold the model weights. But when 100 Users send prompts simultaneously, the system must generate a temporary memory space called the KV Cache to retain the context of those 100 conversations. Instantly, the KV Cache bloats and drains all remaining VRAM. The system throws an Out-Of-Memory (OOM) error and crashes, even though the GPU’s processing power was only 30% utilized. How do you “cram” more Users into the GPU without overflowing RAM? ...