vLLM, TEI power scalable local LLM infrastructure

Self-hosting is evolving from a specialized niche into a viable production standard for high-volume AI applications. vLLM's PagedAttention and continuous batching enable a single A10 GPU to handle thousands of concurrent requests, effectively matching the performance of enterprise-grade managed services. The combination of vLLM and Hugging Face's Text Embeddings Inference (TEI) replaces the managed model lifecycle with a unified, open-source stack that preserves data residency. Leveraging Kubernetes for GPU auto-scaling based on inference queue depth provides the elasticity of serverless APIs while maintaining fixed-cost predictability. Quantization formats like AWQ allow high-performance models like Qwen 2.5 14B to maintain accuracy within constrained hardware footprints.