How it helps your business
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Ollama is a lightweight, extensible framework for building and running large language models (LLMs) locally on your own hardware. By running models locally, organizations can ensure complete data privacy, eliminate API usage costs, and build custom AI-powered applications without relying on third-party cloud services.
Ollama abstracts the complexity of setting up LLMs, providing a simple CLI and REST API to interact with powerful open-weight models like Meta's Llama 3, Google's Gemma, and Mistral. It automatically leverages available hardware acceleration, such as NVIDIA GPUs or Apple Metal, to optimize inference speeds.
For production, Ollama can be deployed via Docker or Kubernetes, integrating smoothly into existing microservices architectures. Combined with frameworks like LangChain or web UIs like Open WebUI, Ollama serves as a robust backend for enterprise AI applications.
Key Benefits
- Data Privacy & Security: Run models entirely on-premise; no data is sent to external APIs.
- Cost Effective: Eliminate recurring cloud AI API costs by utilizing local compute.
- Ease of Use: Simple installation, CLI, and REST API for rapid development.
- Customization: Easily create tailored models using a declarative
Modelfile. - High Performance: Automatic hardware acceleration for optimized inference.
Production Architecture Overview
A production-grade Ollama deployment typically involves:
- Ollama Server: The core engine running the LLMs and exposing the REST API.
- Hardware Acceleration: Kubernetes nodes or VMs equipped with GPUs (NVIDIA/AMD) for fast inference.
- API Gateway / Load Balancer: Nginx or HAProxy to distribute requests across multiple Ollama instances.
- Application Layer: Custom apps, LangChain microservices, or chat interfaces (like Open WebUI) consuming the API.
- Monitoring: Prometheus and Grafana for tracking GPU utilization, response latency, and server health.
- Persistent Storage: Volumes for storing large model weights and configurations.
How we deploy this for you
Security Hardened
Firewalls, SSL, and hardened kernels out of the box.
Performance Tuned
Optimized for speed with cache and DB fine-tuning.
Automated Backups
Daily off-site backups so you never lose your data.
Private Cloud
You own the server and the data. No middleman.
Implementation Blueprint
Prerequisites
# Ensure you have curl installed
sudo apt update && sudo apt install curl -y
# Install NVIDIA Container Toolkit (if using NVIDIA GPUs in Docker)
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart dockershell
Bare Metal / VM Installation
Install Ollama natively on Linux:
curl -fsSL https://ollama.com/install.sh | shshell
The installer automatically enables and starts the
ollama systemd service.Docker Production Deployment
Running Ollama in Docker is highly recommended for containerized environments.
CPU Only Environment
version: "3.8"
services:
ollama:
image: ollama/ollama:latest
container_name: ollama
ports:
- "11434:11434"
volumes:
- ollama-data:/root/.ollama
restart: always
volumes:
ollama-data:yaml
GPU Accelerated Environment (NVIDIA)
version: "3.8"
services:
ollama:
image: ollama/ollama:latest
container_name: ollama
ports:
- "11434:11434"
volumes:
- ollama-data:/root/.ollama
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: all
capabilities: [gpu]
restart: always
volumes:
ollama-data:yaml
Start the service:
docker-compose up -dshell
Managing Models
Once Ollama is running, you need to pull models before you can use them.
# Execute within the container
docker exec -it ollama ollama run llama3shell
This command will download the
llama3 model (if not already present) and start an interactive chat session.To just pull the model for API usage:
docker exec -it ollama ollama pull mistralshell
Interacting via REST API
Ollama exposes a simple REST API on port
11434.# Generate a completion
curl http://localhost:11434/api/generate -d '{
"model": "llama3",
"prompt": "Explain the importance of open-source AI in one paragraph.",
"stream": false
}'shell
Returns a JSON response containing the generated text.
Creating Custom Models (Modelfile)
You can customize models with specific system prompts or parameters. Create a
Modelfile:FROM llama3
# Set the temperature for more creative responses
PARAMETER temperature 0.7
# Set the system message
SYSTEM """
You are a highly skilled DevOps engineer assistant. Provide concise, accurate technical advice.
"""Build the custom model:
docker exec -it ollama ollama create devops-assistant -f /path/to/Modelfileshell
Scaling and Load Balancing
For high-concurrency production setups:
- Deploy multiple Ollama containers/pods across GPU-enabled nodes.
- Place a load balancer (Nginx/HAProxy) in front, configuring it for standard round-robin for stateless generation requests.
- Ensure adequate network bandwidth between the model storage layer and the compute nodes to prevent bottlenecking when loading large models into VRAM.
Monitoring
- GPU Monitoring: Use
nvidia-smiexporter for Prometheus to monitor VRAM usage, GPU load, and power consumption. - API Monitoring: Monitor HTTP status codes and response latencies on the load balancer to track API health.
- Since LLM inference is highly resource-intensive, setting up alerts for GPU memory exhaustion (OOM) is critical.
Includes Security & performance standards
Best place to host Ollama
We recommend Hostinger for its reliability and low cost. It's the perfect home for your new apps, featuring easy setup and 24/7 support.
Get Started on Hostinger