How to effectively deploy Large Language Models for private enterprise data?

All answers to this question.

For a private enterprise setup, the "Gold Standard" is currently using LangChain or LlamaIndex paired with a vector store like Milvus or Pinecone (on-prem). You should host the model using vLLM or Text Generation Inference (TGI) to maximize throughput on your GPUs. Since you have thousands of PDFs, the "chunking" strategy is vital. Use a recursive character splitter and ensure you store metadata for each chunk so the RAG system can cite its sources. This builds trust with users who need to verify the AI's output against the original internal documents.

   Answered 2025-10-15 by Heather Collins

What kind of hardware infrastructure do you have available, and are you planning to use quantization to fit these models on consumer-grade GPUs?

   Answered 2025-10-18 by Joshua Kelly

• We have two A100 GPUs available. We are looking into 4-bit quantization using bitsandbytes to keep the latency low. We want to ensure that the model remains performant enough for real-time employee queries without requiring a massive server farm that would be too expensive to maintain.

     Commented 2025-10-21 by Gregory Fisher

Don't overlook the embedding model. Using a high-quality open-source embedding like BGE or HuggingFace's MTEB leaders will drastically improve your retrieval accuracy.

   Answered 2025-10-24 by Kimberly Ward

• Kimberly is right; the embedding is the foundation. If the vector search fails to find the right context, the LLM will just hallucinate, regardless of how good the prompt is.

     Commented 2025-10-25 by Christopher Moore