Local AI•15 min read
Deploying Local AI: A Guide to Self-Hosted LLMs
Learn how to deploy and run large language models locally for privacy, cost control, and offline capabilities. This guide covers model selection, quantization, hardware requirements, and integration strategies.
Deploying Local AI: A Guide to Self-Hosted LLMs
Running LLMs locally gives you complete control over your data, eliminates API costs at scale, and ensures compliance with data residency requirements.
Why Local AI?
- Privacy: Your data never leaves your infrastructure
- Cost: At scale, local deployment can be significantly cheaper
- Control: No rate limits, no API changes, full customization
- Compliance: Meet data residency and regulatory requirements
Getting Started
Choose the right model for your hardware and use case. Consider quantization to reduce memory requirements. Tools like Ollama and vLLM make local deployment straightforward.