Model Serving

Introduction In production‑grade AI systems, latency is often the decisive factor. A recommendation engine that takes 150 ms to respond may be acceptable for a web page, but the same delay can be catastrophic for an autonomous vehicle or a high‑frequency trading platform. Achieving sub‑10 ms inference while scaling to thousands of requests per second is a non‑trivial engineering challenge that involves careful orchestration of hardware, software, and networking. This article dives deep into how to design, implement, and operate low‑latency inference pipelines using the NVIDIA Triton Inference Server (formerly TensorRT Inference Server) and a distributed model‑serving architecture that guarantees consistency across multiple nodes. We will cover: ...

Introduction If you’re trying to serve large language models (LLMs) efficiently on GPUs, you quickly run into a wall: GPU memory gets eaten by KV cache Throughput collapses as concurrent users increase You spend more on hardware than on your actual application vLLM is an open-source inference engine designed to fix this. It combines: A highly optimized attention implementation (PagedAttention) Continuous batching and scheduling A production-ready API server (OpenAI-compatible) Tight GPU memory management This tutorial is a concise zero-to-hero guide for developers who want to: ...