Tag: local-llm
Browse 3 practical software engineering resources tagged with "local-llm". Discover code recipes, design patterns, documentation templates, and in-depth guides to help you build, deploy, and maintain production-ready solutions involving local-llm. Each resource is written for engineers who ship real systems, with copy-paste examples and practical trade-offs.
Local LLMs
Local LLMs run language models on your own hardware. They offer privacy, offline use, and cost control but require optimization for smaller machines.
The resources below cover quantization, Ollama, llama.cpp, local inference, and model selection. Each guide helps you deploy and use local language models.
Every resource includes clear explanations, copy-paste code, and practical warnings. Use them to make informed decisions, avoid production pitfalls, and speed up your delivery. If you are just getting started, read the beginner-friendly articles first; if you are experienced, jump straight to the advanced patterns and architecture guides. New resources are added regularly, so bookmark this page and check back for the latest patterns.
Run LLMs Locally with Ollama for Private Inference
Install and use Ollama to run open-source LLMs locally with Python, including streaming,...
a Local RAG Pipeline with ChromaDB and Sentence Transformers
Implement retrieval-augmented generation locally with ChromaDB, sentence-transformers embeddings,...
Local LLM Deployment: Ollama, vLLM & llama.cpp
Deploy large language models locally and on-premise. Covers Ollama, vLLM, llama.cpp, quantization,...