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The practical way to run a private, browser-based ChatGPT-style service on a VPS is to separate the chat interface from the model runtime. Deploy Open WebUI as the interface, then connect it to Ollama, vLLM, or an OpenAI-compatible hosted endpoint. Docker makes the interface repeatable; persistent volumes keep accounts, conversations, settings, and downloaded models across restarts. A production deployment also needs HTTPS, authentication, backups, monitoring, and a deliberate decision about where prompts and responses are processed.
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What you are actually deploying
A “ChatGPT clone” is not one program. It is a web application that presents conversations, users, model selection, history, and settings, plus an inference service that loads a language model and generates tokens. Keeping those roles separate lets a modest VPS host the interface while a local GPU, a GPU server, or a hosted API performs the expensive inference.
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The interface: Open WebUI
Open WebUI is a self-hosted AI interface designed to run offline and connect to Ollama, OpenAI-compatible servers, vLLM, and other providers. It stores users, chats, configuration, and integrations in its application data directory. The interface does not magically include a model: it needs a reachable provider before it can generate useful answers.
The inference backend
Ollama is the simplest local runtime for a single machine or a small private installation. It downloads model files, exposes a REST API, and handles loading and generation. vLLM is a serving layer aimed at OpenAI-compatible APIs and higher concurrent throughput. A hosted endpoint can provide the model without downloading or operating it yourself, but prompts and outputs leave your server.
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Why the separation matters
- You can move from CPU inference to a GPU without rebuilding the web interface.
- You can keep the interface on a low-cost VPS while sending generation to another private server.
- You can replace Ollama with vLLM when concurrency or API compatibility becomes important.
- You can back up interface data and model data independently.
Choose where inference will run
Decide the data path and performance target before installing anything. The VPS specification should follow the model runtime, not the other way around.
| Option | Best fit | Data path | Main limitation |
|---|---|---|---|
| CPU-only VPS | Lightweight models, occasional personal use, or the interface only | Prompts stay on the VPS if the model also runs there | Generation becomes increasingly slow as model size and context grow |
| GPU VPS | Responsive generation, larger models, or several users | Prompts are processed on the rented GPU server | Hourly or monthly GPU charges and more driver maintenance |
| Local GPU machine | Frequent use where you already own suitable hardware | Inference can remain inside your home or office network | You maintain drivers, power, cooling, networking, and physical security |
| Hosted model API | Lowest infrastructure effort and no model downloads | Prompts and responses travel to the provider | Usage-based billing and provider-specific retention or terms |
A split deployment is often sensible: Open WebUI runs on the VPS, while Ollama or vLLM runs on a private GPU host. Restrict the model API to a private network, VPN, or allowlisted address; do not make an unauthenticated inference port public.
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Operating system and access
Use a supported 64-bit Linux distribution, create a non-root administrative account, and apply the distribution’s security updates before installing Docker. Use SSH keys rather than a password-only root login. Give the server a stable public address and register a domain name if other people will access the service.
Disk, memory, and network
Reserve disk for the operating system, Docker layers, logs, the Open WebUI database, and model files. Model downloads can occupy many gigabytes, and keeping free space prevents failed upgrades and corrupted caches. CPU inference needs enough system RAM for the model and its context; GPU inference needs enough VRAM for the selected quantization and context window. A VPS that only hosts the interface can be much smaller than one that loads a model.
Install Docker and create a secret
Install Docker Engine and its Compose components using the instructions for your Linux distribution. Generate a long random secret before starting Open WebUI. For example, this produces a 64-character hexadecimal value from 32 random bytes:
openssl rand -hex 32Keep the resulting value in a password manager or deployment secret store. Do not commit it to a public repository, paste it into a screenshot, or reuse it on another installation.
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Deploy Open WebUI with Docker
The documented single-container command below publishes host port 3000, maps the container’s port 8080, persists application data in a named volume, and restarts the container after a reboot:
docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data -e WEBUI_SECRET_KEY=your-secret-key --name open-webui --restart always ghcr.io/open-webui/open-webui:mainReplace your-secret-key with the random value you generated. The open-webui volume holds users, conversations, settings, and other application data. Without that volume, removing or recreating the container can remove the state that makes the service useful.
Verify the container
- Check that Docker reports the container as running with
docker ps. - Read startup output with
docker logs --tail=200 open-webui. Look for a successful bind to port 8080 and resolve any database or permission errors before proceeding. - Open
http://server-address:3000from a trusted network and create the first administrator account. - Confirm that a test conversation remains after restarting the container with
docker restart open-webui.
Port 3000 is suitable for an initial test, not for an exposed production service. Keep it closed to the public internet once a reverse proxy is ready.
Run Ollama for the simplest local backend
Ollama is the shortest path from an empty Linux host to a local model API. Its container listens on port 11434 and stores downloaded models in the named ollama volume.
CPU-only Ollama
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollamaThis keeps model files outside the disposable container. CPU-only inference can work for a lightweight model or occasional requests, but response latency rises with model size, context length, and simultaneous users.
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For NVIDIA acceleration, install the NVIDIA Container Toolkit on the host first, then start the container with access to all GPUs:
docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollamaOllama also documents a Linux installation script:
curl -fsSL https://ollama.com/install.sh | shWhether you use the package or container, performance depends on the actual CPU, memory bandwidth, GPU, VRAM, storage, and model format. A large model may technically load while still being impractical to use without a strong GPU.
Connect Open WebUI to Ollama
- Make sure Ollama is reachable from the Open WebUI container. If both run directly on the host, the host mapping included in the Open WebUI command lets the container address the host through
host.docker.internal. - In Open WebUI, open the administrator settings and the connections or provider area, then add the Ollama API endpoint. For the arrangement above, the endpoint is typically
http://host.docker.internal:11434. - Save the connection and refresh the model list. Download at least one model through Ollama before expecting it to appear.
- Send a short test prompt and watch both the browser response and
docker logs ollamaif the request fails.
If Ollama and Open WebUI are separate containers on a user-defined Docker network, use the Ollama container name as the hostname instead of exposing port 11434 publicly. A public model API gives attackers a direct path to consume compute and may bypass the controls you configured in the interface.
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Use vLLM when serving and concurrency matter
vLLM provides an OpenAI-compatible server and is a better fit when multiple clients, parallel requests, or compatibility with existing OpenAI-style software matter more than the simplicity of Ollama. Its official Docker image is vllm/vllm-openai.
Important vLLM runtime settings
- Set the server host and port explicitly so the API is reachable from Open WebUI. A common internal port is 8000.
- Use a GPU-capable host and install a driver and container runtime compatible with the image.
- Prefer host IPC or an explicit shared-memory allocation. PyTorch uses shared memory for tensor-parallel inference, and a container with too little shared memory can fail even when VRAM appears sufficient.
- Provide the model identifier and any required quantization, context, or tensor-parallel arguments required by the model you selected.
After starting the server, add its OpenAI-compatible base URL in Open WebUI’s provider settings. Keep the API private and apply authentication at the proxy or network layer if the server itself does not enforce it.
Ollama or vLLM?
| Need | Prefer | Reason |
|---|---|---|
| One user, quick setup, local model downloads | Ollama | Minimal runtime configuration and a straightforward REST endpoint |
| Several concurrent users or OpenAI-compatible clients | vLLM | Serving-oriented design and an OpenAI-compatible API |
| Interface on one machine, inference on another | Either | Choose based on the GPU host’s driver, model, and operational requirements |
Connect a hosted OpenAI-compatible endpoint
If the VPS cannot fit the model, Open WebUI can use a hosted OpenAI-compatible provider. The VPS then stores the interface and its database, while generation happens on the provider’s infrastructure. This avoids model downloads and GPU maintenance but changes the privacy boundary: prompts, uploaded documents, and generated responses may be processed by that provider under its retention and usage terms.
Ollama also documents cloud models for cases where local hardware is insufficient. Those models run on Ollama infrastructure and require sign-in. Treat this as a separate data path from a fully local Ollama installation, and explain it to every user who will submit sensitive material.
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Reverse proxy
Use a reverse proxy in front of port 3000. Point a DNS record at the VPS, configure the proxy to forward the chosen hostname to 127.0.0.1:3000 or the private Docker network, and issue a TLS certificate for the hostname. Redirect plain HTTP to HTTPS. Do not expose the administrative interface or model API on separate public ports simply because the containers publish them for local testing.
Application access
Keep Open WebUI authentication enabled before inviting anyone else. Use unique administrator credentials, remove unused accounts, and apply the least privilege available for shared workspaces or model connections. The secret key protects application signing and session-related functions; it does not replace user authentication or HTTPS.
Network boundaries
- Allow inbound SSH only from trusted addresses or through a VPN where practical.
- Allow public access only to the reverse proxy’s TLS port.
- Bind Ollama’s 11434 endpoint and vLLM’s API port to localhost or an internal Docker network unless another private host must reach them.
- Use firewall rules and, for a split deployment, an allowlist containing the interface server’s private address.
Persist and back up the right data
There are at least two independent state sets. The open-webui volume contains chats, users, settings, and connection configuration. The ollama volume contains downloaded model files. Back up both before upgrades, migrations, or changing storage drivers.
A practical backup routine
- Stop or quiesce writes during the backup window so the application database is in a consistent state.
- Create an archive or volume snapshot of
open-webui:/app/backend/data. - Snapshot the Ollama model volume if rebuilding the host would otherwise require downloading every model again.
- Store an encrypted copy outside the VPS account and test restoring to a temporary machine.
- Record the Docker image tag, configuration values, model names, and proxy settings alongside the backup.
Backups that have never been restored are only assumptions. Test a restore after changing the VPS, upgrading Docker, or rotating credentials.
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Size hardware using the model, not the interface
The interface itself is comparatively light; the model determines memory pressure and generation speed. CPU-only systems minimize infrastructure complexity but constrain model size and responsiveness. A GPU VPS is useful when the model or context window does not fit comfortably in CPU RAM, or when several users need fast responses. A local GPU avoids per-hour cloud GPU charges but requires compatible drivers, power, cooling, and physical access.
A concrete performance reference
Ollama’s published scheduler example measured Gemma 3 12B with a 128k context on one NVIDIA RTX 4090 at 85.54 tokens per second, using 21.4 GiB of VRAM with all 49 of 49 layers on the GPU. That is a single 2025 measurement under the stated model and hardware conditions, not a universal speed guarantee or a minimum requirement for every Gemma deployment. Different quantization, prompt length, batching, drivers, and thermals will change the result.
Plan for context and concurrency
- A longer context window increases memory use even when the model parameter count is unchanged.
- Several simultaneous generations need more memory and scheduling headroom than one private chat.
- Leave capacity for the operating system, Docker, the web interface, monitoring, and file caches.
- Measure time to first token and sustained tokens per second separately; a fast stream does not help if requests wait in a long queue.
Production monitoring and maintenance
Open WebUI’s deployment guidance calls for authentication, persistence, backups, and monitoring before opening a deployment to users. Monitor the resources that fail first rather than relying on a single uptime check.
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- Disk utilization and inode usage, especially in Docker and model volumes.
- System RAM, swap activity, GPU VRAM, GPU temperature, and utilization.
- Container restarts, exit codes, and log growth.
- Request latency, time to first token, generation rate, queue depth, and error rates.
- TLS certificate expiry and reverse-proxy health.
Update deliberately
Apply operating-system, Docker, GPU-driver, and image updates on a maintenance schedule. Pin a tested image tag for production rather than silently tracking an unreviewed moving tag, and read release notes before changing the interface or inference runtime. Back up the data volumes first, keep a rollback image available, and verify login, model listing, a new chat, an old chat, and file uploads after the upgrade.
Troubleshoot common failures
The browser cannot open the site
- Run
docker psand confirm thatopen-webuiis running. - Inspect
docker logs open-webuifor a failed migration, invalid secret, or permission error. - Check that the reverse proxy points to port 3000 and that the VPS firewall permits only the proxy’s public port.
- Test the service locally on the VPS before debugging DNS or certificates.
The model list is empty
Check the provider URL from inside the Open WebUI container’s network path, not only from the host. Confirm that Ollama is running, that at least one model has been downloaded, and that the API port is not blocked by a firewall. For vLLM, verify the model server’s startup log and the exact OpenAI-compatible base path configured in Open WebUI.
Generation is extremely slow
Determine whether the model is running entirely on CPU, whether it fits in available VRAM, and whether the context is unusually large. Check GPU utilization and VRAM while generating. Reduce the model size or context for a test, then decide whether a GPU host or a serving runtime designed for concurrency is justified.
The container restarts or the host runs out of space
Inspect container exit codes and host logs, then check disk usage in both named volumes. A full model volume can prevent downloads, database writes, and upgrades. Remove unused images and models only after confirming that they are not needed, and expand the disk before the filesystem reaches 100 percent.
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The container was likely recreated without mounting the persistent open-webui volume, or the volume was deleted. Recreate the container with the same volume name and restore the latest backup if necessary. Model files are separate; restoring only the interface volume will not restore Ollama downloads.
Plan for multiple users and game-development workloads
A private instance can support a small game-development team using it for design-document drafting, code explanation, dialogue iteration, build-script assistance, or internal knowledge search. Those uses still need access controls: a shared account makes it impossible to attribute prompts, revoke one person’s access, or investigate an accidental disclosure.
Set expectations about queueing. One GPU that feels instant for a single developer can become sluggish when several people submit long prompts or request large contexts at once. Start with a small user group, observe latency and VRAM under realistic requests, then decide whether to add a second inference worker, move to vLLM, choose a smaller model, or route overflow to a hosted endpoint.
Check model licenses before sharing access
Model licenses and acceptable-use terms are not uniform across the Ollama catalog or other providers. Read the license for every model you download, especially before offering the service to clients, a studio, a school, or the public. Check whether redistribution, commercial use, fine-tuning, geographic restrictions, attribution, or safety requirements apply. Also review the terms of any hosted endpoint and the privacy obligations attached to user prompts and uploaded files.
A decision framework for your first deployment
- Need only a private interface? Deploy Open WebUI on the VPS and connect it to a model server elsewhere.
- Have modest hardware and occasional use? Start with Ollama and a lightweight model on CPU, accepting higher latency.
- Own or rent a capable NVIDIA GPU? Use Ollama for the simplest path, then benchmark before adding users.
- Expect concurrent requests or need an OpenAI-compatible API? Evaluate vLLM with adequate GPU memory and shared memory.
- Cannot operate or fit local inference? Connect a hosted endpoint and document that prompts leave your server.
- Opening access beyond yourself? Finish HTTPS, authentication, private API networking, tested backups, monitoring, and license review first.
The most maintainable first version is usually Open WebUI in Docker with a persistent volume, Ollama on the same host or a private GPU host, and a reverse proxy terminating HTTPS. That layout gives you a working browser experience while preserving the option to change hardware or inference software later.
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