Running Local LLMs and Optimization
Running large language models locally puts inference power directly in your hands. Instead of relying on cloud APIs with per-token costs and latency, you can deploy transformers, Ollama, or quantized models on your own hardware. This series teaches you how to set up, optimize, and serve local LLMs using Python, covering everything from first steps with Hugging Face to production-grade GPU inference and semantic search with local embeddings.
Whether you're building a chatbot for offline use, fine-tuning a model on proprietary data, or maximizing inference speed under constrained resources, these tutorials show you the complete pipeline. You'll learn when to use Ollama for simplicity, how quantization reduces memory footprint without sacrificing quality, why GPU acceleration matters, and how to deploy your model behind a REST API.
This series assumes you know Python basics and understand what language models do. By the end, you'll be able to download a model, run inference locally, measure performance, and integrate it into a real application.
Articles in this series
- What Is a Local LLM and Why Run One in Python?
- Getting Started with Hugging Face Transformers and Python
- Using Ollama for Simple Local Inference
- Python LLM Quantization: Making Models Smaller and Faster
- GPU Inference with PyTorch and Python
- CPU-Only LLM Inference Optimization in Python
- Prompt Templates and Engineering for Local LLMs
- Building Local Embeddings and Semantic Search with FAISS
- Serving Python LLMs via REST API with Flask
- Optimizing Local LLM Inference: Benchmarking and Tuning