📖 Part III: Hosting LLM Models
🟢 Part 3: Hosting Your Own LLM Models (Full Control & Customization)¶
As chatbot adoption matures, many teams begin to ask a critical question: Should we host our own models? This part of the book explores what happens when you move beyond OpenAI’s playground and take the reins of your own large language model (LLM infrastructure). Whether for data privacy, cost control, or performance tuning, self-hosting opens up new possibilities—along with a fair share of challenges.
We begin by examining why teams choose to self-host, comparing managed platforms like OpenAI with DIY solutions. From there, we dive into hardware selection, cloud platform setup, and open-source model deployments. We’ll walk through the end-to-end process of deploying your own LLM—from model selection to inference endpoint—then move into fine-tuning techniques and advanced performance optimizations.
If you're building a chatbot product that needs full control, enterprise compliance, or custom behavior, this part will teach you the essential skills and tradeoffs of running models yourself.
Chapters Summary¶
🔹 Chapter 10: Introduction to Self-Hosted LLMs¶
Understand the benefits and tradeoffs of self-hosting. Learn when it's worth moving away from OpenAI or Claude to local or cloud-hosted open models. We'll cover privacy, latency, cost, and customization considerations, plus a breakdown of hardware options: GPU, TPU, and CPU setups—on-premise or in the cloud.
🔹 Chapter 11: Hosting Models on Cloud Platforms¶
Explore step-by-step how to host models using AWS SageMaker, Google Vertex AI, and Azure ML. This chapter focuses on creating managed inference endpoints using pre-built or custom container images. You'll learn how to configure autoscaling, deploy multiple model versions, and monitor model health in production.
🔹 Chapter 12: Open-Source Model Hosting (Local & Cloud)¶
Deploy Hugging Face Transformers and other open-source models like LLaMA, Falcon, and Mistral on your own infrastructure. We’ll cover both local setups and hosted endpoints (like Hugging Face Inference Endpoints), and walk through using Docker + FastAPI + GPU acceleration to expose your model to frontend chatbots.
🔹 Chapter 13: Fine-Tuning Your Own Models¶
Learn how to customize pretrained models for your domain using LoRA fine-tuning and other transfer learning methods. This chapter walks through data preparation, using tools like PEFT, and running fine-tuning pipelines with PyTorch. We’ll also cover when to fine-tune vs. prompt-engineer.
🔹 Chapter 14: Advanced Model Optimization Techniques¶
Reduce model size and inference time using quantization, distillation, pruning, and more. Dive into ONNX Runtime, TensorRT, vLLM, and llama.cpp for efficient deployment. This is essential reading if you want real-time responses with limited compute.