How to integrate local LLMs with LangChain for privacy-sensitive data?

All answers to this question.

Running locally is becoming very common. The easiest way is using the Ollama integration. Step 1: Install Ollama and pull your model (e.g., ollama pull llama3). Step 2: In your code, use from langchain_community.llms import Ollama. Step 3: Define your agent as usual. For tool calling, ensure you use a model that has been fine-tuned for function calling, like the "Instruct" versions of Llama 3. While a local 8B or 70B model might not have the "reasoning" depth of GPT-4, they are more than capable of following basic instructions and using local database tools if your prompts are structured clearly.

   Answered 2025-05-17 by Hannah Scott

Does the latency of running a 70B model locally affect the agent's ability to stay "conversational," or is it better to stick to smaller 8B models for real-time interaction?

   Answered 2025-05-20 by Samuel Moore

• Samuel, it really depends on your GPU VRAM. If you have an A100 or H100, the 70B is fast enough. However, for most "office" servers, the 8B or 14B models are much better for agents. When you build AI agents using LangChain on local hardware, you have to balance speed vs. intelligence. For simple data entry or search tasks, an 8B model is snappy and perfectly adequate. For complex logical reasoning, you'll want the 70B and just accept the 2-3 second "thinking" pause.

     Commented 2025-05-22 by Dylan Ortiz

We use vLLM for our local hosting. It’s significantly faster than standard Ollama for serving multiple users simultaneously within our private network.

   Answered 2025-05-24 by Rachel Miller

• vLLM is great for throughput! It's definitely the right choice if you're building a tool for a whole department rather than just a personal project.

     Commented 2025-05-25 by Michael Wagner