Running Local LLMs with Ollama and llama.cpp TL;DR Running LLMs locally gives you privacy, control, and cost savings compared to cloud APIs. This comprehensive guide covers everything you need to deploy production-ready local AI infrastructure using Ollama and llama.cpp. Both tools use GGUF format models with quantization to run efficiently on consumer hardware. Ollama provides a simple REST API and automatic model management, while llama.cpp offers fine-grained control and bleeding-edge features. You can run a 7B parameter model in 4-6GB RAM using Q4_K_M quantization, or larger models with GPU acceleration. ...

TL;DR Install n8n with Docker for self-hosted workflow automation. Quick test: docker run -it --rm -p 5678:5678 n8nio/n8n. Production requires Docker Compose with persistent volumes. For updating existing deployments, see How to Update n8n Docker Container. A production-ready setup requires a docker-compose.yml file that defines persistent storage, sets N8N_EDITOR_BASE_URL for external access, and configures encryption keys. The self-hosted version is free and open-source, giving you full control over data and unlimited workflow executions. You can integrate AI capabilities through dedicated nodes like AI Agent and AI Chain, which connect to OpenAI, Anthropic, or local LLM endpoints. ...

Advanced LLM Parameter Tuning for Production Workloads TL;DR This guide covers advanced parameter tuning techniques beyond basic temperature and top-p settings. For foundational concepts, installation, and basic parameter explanations, see our Complete Guide to Running Local LLMs. Advanced topics covered: dynamic temperature scheduling based on task type, repeat penalty optimization for long-form content, mirostat sampling for consistent output quality, batch processing configuration, and A/B testing parameter combinations in production. ...

TL;DR This guide covers updating existing n8n Docker deployments. For initial installation, see How to Install n8n with Docker for AI Workflow Automation. Updating n8n Docker containers delivers security patches, new AI nodes, and API integration fixes. The core process: pull latest image, backup data, stop container, restart with new version. Total downtime: 2-5 minutes. ...

Hugging Face Skills for Self-Hosting AI with Ollama TL;DR Hugging Face serves as the primary model repository for self-hosted AI deployments, but navigating its ecosystem requires specific skills beyond basic model downloads. You need to understand model cards, quantization formats, and licensing before pulling multi-gigabyte files into your homelab. Start by learning to read model cards on Hugging Face – they contain critical information about context windows, training data, and recommended inference parameters. For Ollama deployments, look for GGUF format models or Modelfiles that reference Hugging Face repositories. LM Studio users should focus on models with clear quantization levels (Q4_K_M, Q5_K_S) that balance quality and VRAM usage. ...

Building llama.cpp from GitHub for Local AI Models TL;DR Building llama.cpp from source gives you a high-performance C/C++ inference engine for running GGUF-format language models locally without cloud dependencies. The process involves cloning the GitHub repository, installing build dependencies like cmake and a C++ compiler, then compiling with hardware acceleration flags for your CPU or GPU. ...

TL;DR Running n8n with Docker Compose gives you a production-ready automation platform for AI workflows without managing complex dependencies. This guide walks through setting up n8n with persistent storage, environment configuration, and AI integrations using OpenAI, Anthropic, and local LLMs. Docker Compose handles multi-container orchestration, making it straightforward to add PostgreSQL for workflow history, Redis for queue management, and reverse proxies for SSL termination. The setup takes under 10 minutes and provides a stable foundation for building AI-powered workflows that process documents, generate content, and orchestrate multi-step automations. ...

OpenClaw Framework in LM Studio for Local AI TL;DR OpenClaw Framework provides a structured approach to building AI-powered command-line tools that integrate with local LLMs running in LM Studio. Instead of sending your terminal commands and system data to cloud APIs, OpenClaw routes everything through your local inference server, keeping sensitive information on your machine. ...

TL;DR n8n offers two deployment paths: self-hosted (free and open-source) or cloud-hosted with tiered pricing. Self-hosted n8n runs on your infrastructure with no licensing fees, while n8n Cloud provides managed hosting across Starter, Pro, and Enterprise tiers with varying execution limits and features. Self-hosted deployments require server management but give you complete control over data, unlimited workflow executions, and no per-execution costs. Install with npm install -g n8n or run via Docker on port 5678. You handle updates, backups, SSL certificates, and scaling. Infrastructure costs depend on your hosting provider and workflow complexity – a basic VPS can run simple workflows, while high-volume automation may need dedicated servers or Kubernetes clusters. ...

What is Ollama: Guide to Running AI Models Locally TL;DR Ollama is a command-line tool that lets you run large language models like Llama, Mistral, and CodeLlama directly on your Linux machine without sending data to external APIs. Install it with a single command, pull models from the ollama.com library, and interact via REST API on port 11434 or through the CLI. ...