Most people think AI = ChatGPT. But for high-security industries like Finance or Healthcare, sending data to a Cloud API is a huge “No.” To solve this, you need to shift from Cloud LLMs to Local LLMs. Here is how you Decode the “No Internet” problem: 1️⃣ Pick an Open-Source Model: Instead of OpenAI, use models like Llama 3 (Meta), Mistral, or Gemma (Google). These are weights you can actually download and own. 2️⃣ Quantization is Key: A massive model won’t fit on standard office servers. Use Quantization (4-bit or 8-bit) to compress the model so it runs fast on local hardware without losing much “intelligence.” 3️⃣ Local Serving Tools: Use frameworks like Ollama, vLLM, or LocalAI. These tools create a local API that works exactly like ChatGPT but stays 100% inside the company’s firewall. 4️⃣ Offline Vector Database: For the RAG pipeline, use local databases like ChromaDB or FAISS. This ensures that even the “search” part of your AI never hits the public web. Privacy isn’t a feature; it’s a requirement. 🛠️ Follow Lets Decode AI to learn how to build AI that works anywhere, even in a bunker. #AI #Privacy #DataSecurity #LocalLLM #Llama3 Ollama SoftwareEngineering GenerativeAI LetsDecodeAI TechCareer

Summary

The video discusses how high-security industries can use Local LLMs instead of Cloud APIs. It outlines steps like selecting open-source models, quantizing them, and using local serving tools and databases to ensure data privacy.

Key Points

1. High-security industries must avoid sending data to Cloud APIs.
2. Choose open-source models like Llama 3 or Mistral for local use.
3. Quantization helps compress models for efficient local performance.
4. Utilize frameworks like Ollama to create local APIs.
5. Implement offline databases like ChromaDB for secure data access.

Tags

Repurpose Ideas

• LinkedIn post: Steps to implement Local LLMs in finance
• Tweet: Key benefits of using Local LLMs over Cloud APIs
• Checklist: Essential tools for building secure AI systems