Local-Llm

TL;DR Jan AI is an open-source desktop application that lets you run large language models entirely on your local machine—no cloud dependencies, no data leaving your network. Think of it as a polished alternative to Ollama with a ChatGPT-like interface built in. What makes Jan different: Unlike command-line tools like llama.cpp or Ollama, Jan provides a complete GUI experience with conversation management, model switching, and system resource monitoring. It supports GGUF model formats and runs models from Llama 3.1, Mistral, Phi-3, and other popular families. ...

TL;DR This guide walks you through deploying a fully offline AI assistant using Ollama and Open WebUI on a Linux system. You’ll run models like Llama 3.1, Mistral, or Qwen locally without internet connectivity or cloud dependencies. What you’ll accomplish: Install Ollama as a systemd service, download AI models for offline use, deploy Open WebUI as your chat interface, and configure everything to work without external network access. The entire stack runs on your hardware—a laptop with 16GB RAM handles 7B models, while 32GB+ systems can run 13B or larger models. ...

TL;DR LM Studio and Ollama are both excellent tools for running LLMs locally, but they serve different use cases. LM Studio offers a polished GUI experience ideal for experimentation and interactive chat, while Ollama provides a streamlined CLI and API-first approach perfect for automation and production deployments. Choose LM Studio if you: ...

TL;DR Running Llama 3 locally with Ollama on Linux takes about 5 minutes from start to finish. You’ll install Ollama, pull the model, and start chatting—all without sending data to external servers. Quick Setup: curl -fsSL https://ollama.com/install.sh | sh # Pull Llama 3 (8B parameter version) ollama pull llama3 # Start chatting ollama run llama3 The 8B model requires ~5GB disk space and 8GB RAM. For the 70B version, you’ll need 40GB disk space and 48GB RAM minimum. Ollama handles quantization automatically, so you don’t need to configure GGUF formats manually. ...

TL;DR - Quick verdict: Ollama for ease-of-use and Docker integration, llama.cpp for maximum control and performance tuning Ollama wins for most self-hosters who want their local LLM running in under 5 minutes. It handles model downloads, GPU acceleration, and exposes a clean OpenAI-compatible API at localhost:11434. Perfect for Docker Compose stacks with Open WebUI, and it integrates seamlessly with tools like Continue.dev for VSCode or n8n workflows. ...

TL;DR Running local LLMs on 8GB RAM systems is entirely feasible in 2026, but requires careful model selection and quantization strategies. Llama 3.2 3B (Q4_K_M quantization) delivers the best balance of capability and efficiency, using approximately 2.3GB RAM while maintaining strong reasoning abilities. Mistral 7B (Q3_K_M) pushes boundaries at 3.8GB RAM, offering superior performance for coding tasks but requiring aggressive quantization. Phi-3 Mini (3.8B parameters, Q4_K_S) sits in the middle at 2.1GB, excelling at structured outputs and JSON generation. ...

TL;DR Ollama transforms your Debian system into a private AI inference server, letting you run models like Llama 3.1, Mistral, and Phi-3 locally without cloud dependencies. This guide walks you through installation, model deployment, API integration, and production hardening. Quick Install: curl -fsSL https://ollama.com/install.sh | sh sudo systemctl enable ollama ollama pull llama3.1:8b ollama run llama3.1:8b You’ll configure Ollama as a systemd service, expose its REST API on port 11434, and integrate it with Open WebUI for a ChatGPT-like interface. We cover GPU acceleration (NVIDIA/AMD), resource limits, and reverse proxy setup with Nginx for secure remote access. ...