Everything you need to know about running LLMs locally
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A practical guide to running open source LLMs on your own hardware, covering the motivations (cost savings, privacy, control), model selection across text, embedding, and vision categories, and a tiered breakdown of deployment tools. Ollama and Ramalama handle simple CLI/GUI serving, LangChain and Podman Desktop AI Lab support app building, and vLLM targets high-concurrency production workloads. Real-world use cases include AI-assisted coding with tools like Roo Code and OpenCode, and automating developer workflows via MCP servers connected to GitHub, Slack, and Kubernetes.
Table of contents
From Ollama to vLLM: A practical guide to selecting, deploying, and scaling local LLMs for privacy, control, and cost savings.Why run your own local & private AI models?Selecting the right model for your use caseRunning your own local LLMsWhat are the best use cases for local LLMs?Wrapping up & next stepsMore from We Love Open SourceAbout the AuthorSort: