AI Serving

Large Language Models (LLMs) power everything from chatbots to code generation, but scaling them for training, fine-tuning, and inference demands distributed computing expertise. Ray, an open-source framework, simplifies this with libraries like Ray LLM, Ray Serve, Ray Train, and Ray Data, enabling efficient handling of massive workloads across GPU clusters.[1][5] This guide takes you from zero knowledge to hero status, covering installation, core concepts, hands-on examples, and production deployment. What is Ray and Why Use It for LLMs? Ray is a unified framework for scaling AI and Python workloads, eliminating the need for multiple tools across your ML pipeline.[5] For LLMs, Ray LLM builds on Ray to optimize training and serving through distributed execution, model parallelism, and high-performance inference.[1] ...

As an expert LLM infrastructure engineer, I’ve deployed countless inference systems where time-to-first-token (TTFT) and GPU efficiency make or break production performance. Enter LMCache—a game-changing KV cache layer that delivers 3-10x delay reductions by enabling “prefill-once, reuse-everywhere” semantics across serving engines like vLLM.[1][2] This zero-to-hero tutorial takes you from conceptual understanding to production deployment, covering architecture, integration, pitfalls, and real-world wins. Whether you’re building multi-turn chatbots or RAG pipelines, LMCache will transform your LLM serving stack. ...