Deployment

TL;DR Nvidia’s Vera CPU architecture brings ARM-based processing designed specifically for AI workloads to self-hosted environments. Unlike traditional x86 chips, Vera integrates neural processing units directly into the CPU die, making it particularly effective for running multiple Ollama instances simultaneously without GPU bottlenecks. For homelab operators, this means you can run agent frameworks like AutoGen or LangChain with local LLMs while maintaining responsive system performance. A typical setup might run three Ollama instances – one for code generation with codellama:13b, another for general tasks with llama2:13b, and a third for function calling with mistral:7b – all on a single Vera-based system without thermal throttling. ...

Running Qwen2.5 Models Locally with Ollama TL;DR Qwen2.5 models from Alibaba Cloud offer exceptional bilingual performance in Chinese and English, with particular strengths in coding, mathematics, and multilingual reasoning tasks. Unlike Llama models, Qwen2.5 variants excel at code generation across multiple programming languages and demonstrate superior performance on mathematical problem-solving benchmarks. The model family ranges from the compact 0.5B parameter version suitable for edge devices to the powerful 72B parameter variant for complex reasoning tasks. ...

Install LM Studio for Local AI Model Hosting TL;DR LM Studio is a desktop GUI application that lets you run large language models locally without sending data to cloud providers. Download the installer from lmstudio.ai for your operating system – it supports macOS, Windows, and Linux. The application is free for personal use and provides a user-friendly interface for downloading models from Hugging Face and running them on your hardware. ...

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. ...

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. ...

Running a Private AI API for Your Business TL;DR You can run your own OpenAI-compatible API on a single machine with a GPU. Your data never leaves your hardware, costs are fixed instead of per-token, and you can serve custom fine-tuned models. What you get: A drop-in replacement for the OpenAI API (change one line of code to switch) Complete data privacy — nothing sent to external servers Fixed monthly cost instead of unpredictable per-token billing Custom models fine-tuned on your business data No per-seat licensing Minimum setup: ...

Jan AI: Guide to Self-Hosting LLMs on Your Machine 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. ...

LM Studio vs Ollama: Complete Comparison for Local AI 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. ...

How to Run Llama 3 Locally with Ollama on Linux 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. ...

Self-Hosting Open WebUI with Docker TL;DR Open WebUI is a self-hosted web interface for running local LLMs through Ollama, providing a ChatGPT-like experience without cloud dependencies. This guide walks you through Docker-based deployment, configuration, and integration with local models. What you’ll accomplish: Deploy Open WebUI in under 10 minutes using Docker Compose, connect it to Ollama for model inference, configure authentication, and set up persistent storage for chat history and model configurations. ...