Architecting Low‑Latency Inference Pipelines for Real‑Time High‑Throughput Language Model Applications
Table of Contents Introduction Latency vs. Throughput: Core Trade‑offs Key Building Blocks of an LLM Inference Pipeline 3.1 Hardware Layer 3.2 Model Optimizations 3.3 Serving & Orchestration Batching Strategies for Real‑Time Traffic Asynchronous & Streaming Inference Scalable Architecture Patterns 6.1 Horizontal Scaling with Stateless Workers 6.2 Edge‑First Deployment Observability, Monitoring, and Auto‑Scaling Practical Code Walkthroughs 8.1 Quantized Inference with 🤗 BitsAndBytes 8.2 FastAPI + Triton Async Client 8.3 Dynamic Batching with NVIDIA Triton Real‑World Case Study: Conversational AI at Scale Best‑Practice Checklist Conclusion Resources Introduction Large language models (LLMs) have moved from research prototypes to production‑grade services powering chatbots, code assistants, search augmentation, and real‑time translation. While model size and capability have exploded, user experience hinges on latency—the time between a request and the model’s first token. At the same time, many applications demand high throughput, processing thousands of concurrent queries per second (QPS). ...