Relevance Lab - Private LLM Server with Ollama and RAG

Enterprise Private LLM Server with Ollama is a preconfigured Amazon Linux 2023 AMI that delivers a complete private AI development platform in a single deployable image. Unlike generic GPU AMIs or manual Ollama installations, this solution ships Ollama pre-integrated with a full RAG pipeline, GPU-accelerated inference stack, remote desktop access via NICE DCV, and notebook environments - eliminating days of configuration so teams can focus on building AI applications.

Organizations can build, test, and deploy private AI assistants, Retrieval-Augmented Generation (RAG) applications, document intelligence solutions, and machine learning models without configuring operating systems, drivers, development environments, or AI dependencies.

A financial services team managing thousands of internal compliance documents can deploy this AMI to build a private document intelligence pipeline:

1. Ingest - Load documents from Amazon S3 into the processing pipeline
2. Embed - Generate vector embeddings using Hugging Face Transformers and FAISS
3. Retrieve - Query relevant document sections using LangChain or LlamaIndex
4. Generate - Produce accurate, context-grounded answers using a locally-hosted LLM via Ollama

All data remains within your VPC - model weights and inference data never leave your AWS environment.

Private LLM Deployment with Ollama Run open-source Large Language Models directly within your AWS environment. Ollama is preconfigured and ready to pull and serve models immediately after launch, keeping all inference data within your infrastructure.

GPU-Accelerated AI Environment Preconfigured NVIDIA Driver, CUDA Toolkit, and cuDNN support accelerated model inference and fine-tuning on AWS GPU instances. Recommended instance families include g5 (cost-effective inference) and p4d (large-scale training).

Complete RAG Development Stack Includes Ollama, Hugging Face Transformers, LangChain, LlamaIndex, FAISS, LoRA/PEFT, FastAPI, MLflow, MONAI, and Weights & Biases - a unified stack for building enterprise search, knowledge management, and conversational AI solutions.

Integrated Development Environment Visual Studio Code, PyCharm Community Edition, RStudio Desktop, Jupyter Notebook, and JupyterLab provide end-to-end AI development workflows accessible through NICE DCV remote desktop.

Containerized AI Workloads Docker and Docker Compose are preconfigured for deploying scalable AI applications and microservices.

AWS-Native Integration Compatible with Amazon Bedrock, Amazon SageMaker, Amazon OpenSearch Service, Amazon S3, and other AWS services commonly used in AI architectures.

• All LLM inference runs locally within your VPC with no external API calls
• NICE DCV provides encrypted remote desktop access
• No model weights or inference data leave your AWS environment
• Compatible with VPC-only deployments for network isolation
• Customers maintain full control over security groups, IAM policies, and encryption configuration

• g5.xlarge - Small to medium model inference (up to 7B parameters)
• g5.2xlarge - Medium model inference and light fine-tuning
• g5.4xlarge / g5.12xlarge - Large model inference (13B+ parameters)
• p4d.24xlarge - Large-scale training and fine-tuning workloads

Minimum requirement: GPU instance with NVIDIA GPU and sufficient GPU memory for your target model size.

• OS: Amazon Linux 2023
• Remote Access: Amazon NICE DCV
• Languages: Python 3.x, R
• AI Frameworks: Ollama, Hugging Face Transformers, LangChain, LlamaIndex, FAISS, LoRA/PEFT, FastAPI, MLflow, MONAI, Weights & Biases
• ML Libraries: PyTorch, TensorFlow, Scikit-learn, PySpark, Dask, Vowpal Wabbit
• GPU Stack: NVIDIA Driver, CUDA Toolkit, cuDNN
• Containers: Docker, Docker Compose
• Tools: VS Code, PyCharm CE, RStudio, JupyterLab, Git, AWS CLI, Chrome

1. Launch the AMI on a supported GPU instance (g5.xlarge or higher recommended)
2. Connect via NICE DCV remote desktop (port 8443)
3. Open a terminal and run Ollama to pull your preferred model
4. Start building with JupyterLab, VS Code, or the framework of your choice

To validate the solution for your workload, launch on a g5.xlarge instance and run the included sample RAG notebook that demonstrates the full Ingest-Embed-Retrieve-Generate pipeline using a locally-hosted model. The notebook walks through document ingestion from S3, embedding generation, FAISS indexing, and answer generation - providing a hands-on proof of concept within a single session.

For teams requiring a guided evaluation or architecture consultation before deployment, contact the Relevance Lab team through the support channel to schedule a pilot session tailored to your use case.