Part 6: Enterprise vLLM Deployment, Quantization & Automated Evals
← Previous Chapter: Part 5: Preference Alignment | Series Hub Prerequisite: Read Part 5: Preference Alignment with DPO & GRPO for preference alignment and JSON schema enforcement. Answer-first: High-throughput enterprise SLM serving overcomes the GPU Memory Wall via vLLM PagedAttention v2, Multi-Head Latent Attention KV cache compression, and AWQ 4-bit quantization. Coupled with dynamic Multi-LoRA serving via Punica CUDA kernels and automated CI/CD eval gates, a single 24GB commodity GPU sustains hundreds of concurrent streams at P99 latencies under 35ms. ...