Running Ollama in Docker: Setup, GPU, and When to Go Managed
Ollama in Docker gives you reproducible LLM inference on your own hardware. But Docker ops is hard: GPU passthrough, memory limits, data volumes, OS patching, and exposed APIs. Teams running Ollama containers end up managing infrastructure instead of using LLMs. Opsily removes the ops. Deploy in 4 minutes instead of 4 days.
Why Opsily Beats DIY Ollama in Docker
Ollama in Docker is powerful. But 'docker run' becomes 'docker-compose', which becomes Kubernetes, which becomes a full ops team. Here's what changes when we host it for you.
GPU configured from day one
NVIDIA GPU passthrough is automatic on Opsily. No CUDA dependencies, no '--gpus all' debugging. Your LLM inference runs at full speed. DIY Docker requires manual nvidia-docker setup, driver matching, and host-level configuration. We handle it so you don't.
OS updates and security patching: automatic
Ollama containers need a host OS. That OS needs patching every week. DIY means you patch it or ignore security risks. Opsily patches every server daily. No 3am security alerts. No downtime. Your Ollama keeps running.
Your data stays private. In EU.
Docker on your server = your data is your problem. Self-hosted Ollama in Germany meets GDPR. Opsily keeps your models and API calls in EU data centers. No third-party inference API. No vendor lock-in. GDPR compliance by default.
Built for teams who need reliability
How Docker Ollama Works
If you're running Ollama in containers yourself, here's the path most teams take. And where things get complex.
Choose Your App
Select an app to get started.
Write a Dockerfile
Start from the Ollama base image. Add your models as RUN ollama pull llama2. Expose port 11434 for the API. Your container is now a reproducible LLM inference box.
Configure Docker Compose
Single container is fine for local dev. Production needs Ollama + Open WebUI together. Compose adds networking, volume mounts, environment variables, and restart policies. Now you have a stack.
Add GPU and memory management
If you have NVIDIA GPU: add runtime: nvidia and device_ids. Map /root/.ollama volumes for model storage. Set memory limits so containers don't OOM. Production Ollama models eat 8-16GB RAM for 7B-13B sizes.
Deploy to production and maintain
Push to your server. Configure Ollama API endpoint. Expose it safely (reverse proxy, API key, TLS). Then patch the host OS, manage model updates, monitor GPU utilization, and scale when you outgrow the container. This is where DIY becomes expensive.
Apps That Pair with Ollama
Ollama alone is just an API. These frontends turn it into a tool your team uses every day.
Self-hosted chat interface for local and private LLMs
Enhanced ChatGPT Clone: Multi-LLM Chatbots with Modular AI
Private AI document chat that works with any LLM, anywhere
Ollama vs Other Containerized LLMs
If you're evaluating container-based LLM inference, you have options:
Ollama: Most popular. Pre-built binaries. Easy model management (ollama pull llama2). OpenAI API compatibility for quick migration. Works on Mac, Linux, Windows. 60K+ GitHub stars. Active community.
Vllm: Raw performance. Better for batching. Harder to set up. No model manager like Ollama. Popular in ML labs, not ops teams.
LocalAI: Open source alternative. Slower startup. No official Docker images. Community-maintained. Smaller ecosystem.
LM Studio: Desktop GUI only. Not containerized. Good for solo experimentation. Can't scale to teams.
Ollama wins for production Docker setups. But Docker operations still cost you: time to debug nvidia-docker, memory tuning, model storage on disk, API endpoint security, monthly OS patching, backup strategy.
Docker Compose: The Setup Most Teams Try
Here's a typical Docker Compose stack for running Ollama with Open WebUI in production:
version: '3.8', services: ollama (image: ollama/ollama:latest, runtime: nvidia, ports: 11434:11434, volumes: ollama_data:/root/.ollama, restart: unless-stopped) and open-webui (image: ghcr.io/open-webui/open-webui:latest, ports: 3000:8080, environment: OLLAMA_BASE_URL=http://ollama:11434, depends_on: ollama, restart: unless-stopped).
This works locally. But production adds: secrets management, reverse proxy (Nginx), TLS certs, API key auth, monitoring, log shipping, backup cron jobs, and disaster recovery playbooks. That 5-minute setup becomes a 40-hour project.
When to Stay on DIY Docker
If your team already runs Kubernetes or has ops engineers on payroll: DIY Docker might make sense. You have the staff to maintain it.
If you're prototyping: local Docker is fast and free.
If you have no GPU at home and want to experiment: Docker on a rented VPS costs $5-10/mo for compute. Add your ops time, and you're already near our $20/mo flat price for Opsily hosting.
When to Switch to Managed Opsily Hosting
- You're tired of patching - OS updates every week, security alerts, testing before rollout.
- GPU setup is annoying - nvidia-docker, driver version matching, CUDA compatibility checks.
- You need GDPR - EU data residency, no third-party inference APIs, your models stay yours.
- Your team is growing - Docker support ticket means context switch and productivity loss.
- Your Ollama is business-critical - paid support, uptime SLA, automatic backups, disaster recovery.
If any of these sound familiar, Opsily's $20/mo (or $40-70 for more capacity) saves you money within your first month of not maintaining it yourself.
Ollama Docker DIY vs Opsily Managed
Pricing as of June 2026. DIY cost includes labor; Opsily is all-in.
Simple, Transparent Pricing
All plans include GDPR-compliant German hosting, automatic patching, daily backups, and 99.9% uptime. No hidden fees. Scale up anytime.
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Trust & Compliance
Opsily meets the security and privacy standards that teams running Ollama care about.
GDPR Compliant
Data residency in German data centers. No data transfers to third parties. Full compliance with GDPR article 32 security requirements.
Data Encryption
AES-256 encryption at rest. TLS 1.3 in transit. Your Ollama models and inference logs are encrypted on disk.
Automated Security Updates
Every server patched daily. Security vulnerabilities fixed within 24 hours of release. No downtime patching on Opsily infrastructure.
EU Infrastructure
Servers in Frankfurt, Germany. Owned and managed by Opsily. No third-party cloud rent. Full control of your data.
Open Source Transparency
Ollama is open source (MIT license). Open WebUI is open source. LibreChat is open source. No proprietary LLMs reading your data.
Frequently Asked Questions
Pull the official Ollama Docker image, then run: docker run -d -v ollama:/root/.ollama -p 11434:11434 ollama/ollama. This exposes Ollama's API on port 11434. To load models, run docker exec <container> ollama pull llama2. For production, use Docker Compose to manage Ollama and Open WebUI together. See our setup guide for the full Compose file.
Ready to skip the Docker ops?
Your Opsily instance is ready in 4 minutes. Ollama runs. GPU is configured. Updates are automatic. No Docker debugging. No weekend patching.