On‑Prem RAG & Agent Applications
Build enterprise-grade RAG and agent workflows entirely on-premises — covering compute setup, vector databases, and agent orchestration.
Learn how to design on-prem RAG systems with full control over data, hardware, deployment, and observability.
- Hardware & infra planning: GPUs vs CPUs, VRAM, orchestration
- Hosting LLMs & embeddings locally vs using cloud APIs
- Vector database selection and indexing
- Building agent pipelines (RAG + reasoning agents)
- Observability and evaluation with tools like LangSmith, Langfuse
Resources:
-
Plural.sh: Self‑Hosted LLM: A Practical Guide for DevOps – covers setup using OpenLLM, HuggingFace, Ray Serve on Kubernetes https://www.plural.sh/blog/self-hosting-large-language-models/?utm_source=chatgpt.com
-
Building a Fully On‑Premises GenAI Stack – walks model selection, orchestration, vector DB, observability https://medium.com/%40bhargavaganti/building-a-fully-on-premises-genai-stack-your-ultimate-guide-to-self-hosted-llms-embeddings-ocr-6c32a1a1372b
-
EyeLevel.ai: How to Build a RAG System on Prem – details pipelining ingest, microservices, GPU orchestration for on‑prem RAG https://www.eyelevel.ai/post/how-to-build-a-rag-system-on-prem?utm_source=chatgpt.com
-
Langfuse docs: Self‑host LLM observability – shows deploying observability tooling on your stack https://langfuse.com/self-hosting?utm_source=chatgpt.com
Suggested Project Build a mini end‑to‑end pipeline: ingest documents with OCR, embed them, serve a quiz bot or Q&A agent locally, monitor via Langfuse.