How to Build a Fully Private Local AI Stack Using Ollama, LM Studio and Continue

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Developers can now run capable AI models entirely on local hardware using tools like Ollama, LM Studio, and the Continue extension for VS Code, eliminating recurring API costs. Advanced quantization formats such as GGUF, EXL2, and AWQ make it possible to deploy large language models on consumer-grade machines including Apple Silicon Macs and Linux servers. Ollama serves as a lightweight background daemon suited for automation, while LM Studio offers a desktop GUI for interactive model testing and hardware monitoring. Together, these tools support OpenAI-compatible API endpoints, enabling private RAG pipelines, secure enterprise workflows, and local coding assistants. The author recommends running both tools in parallel and routing prompts through IDE agents like Continue to replicate cloud-based AI capabilities without sending data to external servers.

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