Yes—you can run a private, browser-based AI assistant on a Linux VPS. The practical stack is Docker, Open WebUI for the ChatGPT-like interface, and either Ollama running models on the VPS or an OpenAI-compatible remote inference endpoint. A small VPS is enough for the interface and a remote endpoint; local model inference needs substantially more memory, storage, and operational care.
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This guide covers the architecture, VPS sizing, Docker deployment, Ollama connectivity, HTTPS, access control, backups, updates, and the failure modes that most often break an internet-facing installation.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsWhat a self-hosted AI assistant on a VPS actually is
A VPS is an always-on Linux server rented from a hosting provider. You administer its operating system, firewall, containers, storage, and accounts. Open WebUI runs in a Docker container and supplies the web application: user accounts, conversations, settings, model selection, and optional document or tool features. Its official project description calls it “a self-hosted AI platform that’s extensible, feature-rich, user-friendly, and built to run entirely offline” (Open WebUI official home page, accessed October 14, 2026).
Open WebUI is not itself a language model. It sends prompts to a model provider. That provider can be Ollama on the same VPS, another machine on your private network, or an OpenAI-compatible endpoint operated elsewhere. Keeping those roles separate makes the deployment easier to resize: the web interface can remain on a modest server while inference moves to a larger CPU/GPU machine or a managed API.
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Why a VPS is useful for a gaming-focused assistant
An always-on server lets you reach the same assistant from a desktop, phone, or gaming laptop without leaving a home computer powered on. You can use it for game-mechanics notes, lore searches over your own documents, build-planning conversations, mod documentation, or general productivity around a gaming community. These are examples of possible workflows, not a guarantee that every model knows a particular game or can safely execute tools.
The trade-off is responsibility. The VPS is an internet-facing computer containing conversations, uploaded files, user accounts, and possibly credentials for connected services. You must secure it, patch it, monitor capacity, and maintain recoverable backups.
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Decide where prompts are processed before selecting a VPS size. Local inference keeps data and model files under your control but consumes the server’s CPU, RAM, disk, and sometimes GPU. A remote compatible endpoint reduces local resource requirements and may provide stronger or faster models, but prompts and files leave your infrastructure and you become dependent on the provider’s billing, availability, and data-handling terms.
| Consideration | Ollama on your VPS | Remote OpenAI-compatible endpoint |
|---|---|---|
| Privacy and residency | Prompts, responses, and model files can remain on infrastructure you administer. Your logs and backups still need protection. | Requests are sent to a third party. Review its retention, training, access, and geographic-processing policies. |
| Recurring cost | No per-request API bill, but you pay for a larger VPS, storage, and possibly GPU capacity. | Smaller VPS bill, plus provider charges that may be usage-based or subscription-based. |
| Latency | Depends on VPS CPU/GPU, model size, context length, and whether the server is swapping. | Depends on your users’ distance from the endpoint, network path, provider load, and model queue. |
| Model choice and quality | Limited to models that fit your available memory, storage, and supported runtime. | Often offers a wider catalog, but models and limits can change without your control. |
| Operations | You download, update, monitor, and back up model files and caches. | The provider operates inference; you still operate Open WebUI, credentials, users, and backups. |
| Reliability | A single VPS outage affects both interface and inference unless you build redundancy. | The interface can remain available while the endpoint is maintained, but provider outages still stop responses. |
A common first deployment is Open WebUI on a small VPS connected to a remote endpoint. Move Ollama onto the VPS only after you have verified the application’s authentication, backup, and update procedures.
How much RAM and CPU do you need?
A 2026 VPS deployment guide gives these starting points: 2 vCPUs and 4 GB of RAM for development or testing, and 4 vCPUs, 8 GB of RAM, and at least 20 GB of SSD storage for production (TechRadar Pro, 2026). These are baselines for the application and its ordinary supporting services, not performance guarantees and not a promise that a local model will fit.
| Use case | Starting allocation | What it does and does not cover |
|---|---|---|
| Development/testing | 2 vCPUs, 4 GB RAM | Suitable for learning Docker and configuring Open WebUI, especially when inference is remote. Expect limited headroom for local models or multiple users. |
| Production interface with remote inference | 4 vCPUs, 8 GB RAM, at least 20 GB SSD | A practical starting point for the UI, database-like application data, logs, and moderate use. Keep extra room for updates and backups. |
| Production with local Ollama | More than the baseline; size from the model and workload | RAM, model quantization, context length, simultaneous chats, embeddings, retrieval jobs, and CPU/GPU availability determine the real requirement. |
Local Ollama inference can need significantly more RAM than the interface. A larger model, a longer conversation context, several concurrent users, or an embedding job can push a server into swap or an out-of-memory kill even when the web page itself is responsive. Plan storage for the operating system, Docker layers, Open WebUI data, model files, caches, logs, and backups; do not treat the advertised model size as the complete disk requirement.
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Signals that the VPS is undersized
- Responses become progressively slower while swap usage rises.
- The kernel or container runtime reports an out-of-memory kill when a model loads.
- Disk usage approaches 100 percent after downloading models or generating logs.
- One user’s long context makes every other conversation sluggish.
- Embedding or retrieval jobs compete with inference and make the interface time out.
The remedies are to resize the VPS, use a smaller or more aggressively quantized model, reduce context and concurrency, move inference to a remote endpoint, or separate the interface and model server onto different machines.
Prepare the VPS before installing anything
1. Select region, storage, and access
Choose a Linux VPS region close to the people who will use the assistant. Distance affects page loads and the time required to send prompts and receive streamed tokens. Select SSD-backed storage large enough for the OS, containers, model files, logs, and at least one backup staging area. If you expect uploads or several local models, leave capacity for growth rather than filling the initial disk.
Record the server’s public IP, operating-system version, administrator contact, and recovery-console procedure. Use SSH keys where available. Create a normal administrator account and reserve the root account for recovery; do not run day-to-day work as root.
2. Patch the operating system and configure a firewall
Apply all available operating-system security updates before exposing services. Enable a host firewall with a default-deny inbound policy, allowing SSH only from the addresses or VPN through which you administer the server. Later, allow HTTP and HTTPS for the reverse proxy. Do not open Ollama’s API port or a Docker socket to the public internet.
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Keep a tested emergency path: a provider console, a second administrator account, or another documented way to recover if a firewall rule blocks SSH. Save the firewall configuration as part of your deployment notes.
3. Install Docker Engine and Compose
Install Docker Engine and the Compose plugin from your Linux distribution or Docker’s official instructions. Verify that your administrator can run Docker as intended, then check the installed versions. Keep Docker itself patched; a secure application container cannot compensate for an outdated host runtime.
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Deploy Open WebUI with persistent data
Open WebUI’s documented Docker example maps host port 3000 to the container’s port 8080 and persists application data in an open-webui volume. Persistence is essential: the volume contains conversations, users, settings, and potentially uploaded documents. Recreating a container without that mount creates what looks like a fresh installation.
1. Create a deployment directory and secret
On the VPS, create a directory readable only by the administrator. Generate a long random signing secret:
mkdir -p ~/open-webui
cd ~/open-webui
openssl rand -hex 32Copy the output into a protected environment file or your secret manager. Pass it as WEBUI_SECRET_KEY and keep the same value across container recreation. Do not commit it to a Git repository, paste it into a public issue, or place it in a world-readable shell history.
2. Start the container
A minimal Compose file can look like this:
services:
open-webui:
image: ghcr.io/open-webui/open-webui:main
container_name: open-webui
ports:
- "3000:8080"
environment:
WEBUI_SECRET_KEY: ${WEBUI_SECRET_KEY}
volumes:
- open-webui:/app/backend/data
restart: unless-stopped
volumes:
open-webui:Store the secret in a local .env file with restrictive permissions, then start the service:
chmod 600 .env
docker compose up -d
docker compose ps
docker compose logs --tail=100 open-webuiThe example exposes port 3000 on every host interface. During initial setup, restrict that port with the firewall or bind it only to a private interface. For a public service, put Open WebUI behind a reverse proxy that terminates HTTPS instead of treating the raw container port as your final internet-facing endpoint.
3. Complete first-run onboarding
- Open the service from an administration workstation and create the first account. The first account becomes the administrator.
- Confirm that you can sign out and sign back in from a separate client.
- Disable public registration after onboarding unless you intentionally need open sign-ups. Create named accounts for additional users instead.
- Check that a test conversation survives
docker compose downfollowed bydocker compose up -d. Do not remove the named volume during this test.
Connect Ollama running on the VPS
If Ollama runs directly on the host while Open WebUI runs in Docker, the container must be able to reach the host’s Ollama listener. The official quick-start guidance notes that host Ollama must listen on 0.0.0.0 in this arrangement. Configure the listener deliberately and restrict access with firewall rules; binding to all interfaces does not mean the API should be reachable from the public internet.
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Connectivity checklist
- Confirm Ollama is installed and running on the host.
- Confirm it is listening on the interface and port expected by your Open WebUI configuration.
- From inside the Open WebUI container, test name resolution and TCP reachability to the host.
- In Open WebUI’s connection settings, enter the host address reachable from the container, not automatically
localhost. Inside a container,localhostrefers to that container. - Download a model only after you have verified disk and RAM headroom.
- Run a short prompt, then observe CPU, memory, swap, and disk use while the model is loaded.
Never publish the Ollama API directly to the internet. Open WebUI should be the authenticated front end, and host firewall rules should limit model-server access to the Docker network or other trusted sources.
Connect an OpenAI-compatible remote endpoint
For remote inference, configure Open WebUI with the endpoint’s base URL, model identifier, and authentication credential. Store the credential as a secret rather than embedding it in a public Compose file. Use a dedicated key with the smallest available permissions and a spending or usage limit when the provider supports one.
Test with a non-sensitive prompt first. Verify which model answered, whether streaming works, how errors are displayed, and whether uploaded documents are sent to the endpoint. A remote endpoint changes the privacy boundary: your VPS may be well secured while the text still leaves it for processing.
Put HTTPS and authentication in front of the service
Reverse proxy and DNS
Point a domain or subdomain at the VPS, then configure a reverse proxy to forward HTTPS traffic to Open WebUI’s internal port. Obtain a trusted TLS certificate and redirect HTTP to HTTPS. Keep the container port private to the host or Docker network once the proxy is working.
Test from a separate network, such as a phone connection, rather than assuming that a browser on the VPS proves public access. Check that the certificate covers the exact hostname, that HTTP redirects, and that WebSocket or streaming responses remain functional through the proxy.
Accounts, sessions, and administrator access
- Use a unique, high-entropy password for every account and protect administrator accounts with your strongest available authentication controls.
- Disable public registration after onboarding unless open registration is a deliberate requirement.
- Give ordinary users only the access they need. Do not share the administrator account.
- Review active sessions and revoke them when an account or secret may have been exposed.
- Keep administration behind an allowlist, VPN, or an additional identity-control layer when practical.
Open WebUI’s hardening guidance warns that authenticated users may gain access to inference, tool execution, and code pipelines. Authentication alone is therefore not a substitute for authorization, careful tool configuration, and host isolation. Never give the application a Docker socket or unrestricted host credentials merely to make an integration convenient.
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Backups, updates, and recovery
Back up the data that makes the assistant yours
The persistent volume can contain chats, users, settings, and uploaded documents. Back it up to encrypted storage separate from the VPS. Define how long backups are retained and who can restore them. A backup that has never been restored is only an assumption, so schedule a test restore to a temporary location.
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Back up configuration files and secret-management instructions without exposing secret values. Model caches can often be downloaded again, but downloading them after a disaster consumes time and bandwidth; decide whether your recovery objective requires copying them as well.
Update with a rollback path
- Read the release notes for Open WebUI, Docker, the operating system, and any model runtime you plan to update.
- Verify a recent backup and record the currently running image tag and configuration.
- Pull the new image and recreate the container without deleting the persistent volume.
- Check login, an existing conversation, model connectivity, file uploads, and streaming responses.
- If a change fails, redeploy the recorded image version and restore the data volume only if the upgrade changed data incompatibly.
Do not use an unpinned moving image tag as your only change-management record. Record the image digest or release identifier in your maintenance notes so you know exactly what was running.
Monitor the boring things
Alert on disk usage, memory pressure, swap activity, container restarts, certificate expiry, failed backups, and unusual authentication failures. Keep logs from growing without limit, but retain enough security and operational history to investigate an incident. A full filesystem can stop the interface even when CPU and RAM look healthy.
Common failures and precise fixes
| Symptom | Likely cause | Fix |
|---|---|---|
| Open WebUI cannot reach Ollama | The container is using the wrong host address, or Ollama is bound only to loopback. | Use the host address reachable from the container, configure Ollama to listen on the required interface, and allow only trusted network traffic through the firewall. The documented Docker arrangement requires host Ollama to listen on 0.0.0.0. |
| The model process is killed | RAM is exhausted by the model, context, embeddings, or concurrent users. | Resize the VPS, reduce concurrency or context length, choose a smaller quantized model, stop competing jobs, or move inference to a remote endpoint. |
| Conversations disappear after recreation | The open-webui volume was omitted, renamed, or deleted. |
Inspect docker compose config and the container’s mounts, restore the correct volume backup, and test persistence before future upgrades. |
| Strangers use the service | A port was forwarded without authentication, HTTPS, or adequate firewall rules. | Remove public access to the raw container port, enforce HTTPS and login through the proxy, disable registration, rotate exposed credentials, and review logs for misuse. |
| Responses are painfully slow | CPU-only inference, a distant endpoint, disk swapping, an oversized model, or a long context. | Measure CPU, RAM, swap, and network latency; move to a smaller model or closer endpoint, reduce context, resize the VPS, or separate UI and inference. |
| The site works locally but not from the internet | DNS, firewall, proxy, certificate, or provider networking is incomplete. | Verify DNS resolution, inbound 80/443 rules, proxy upstream address, certificate hostname, and external testing from a different network. |
A production-readiness checklist
- Linux host patched; non-root administrator created; SSH access tested.
- Firewall defaults to deny; only required administration and HTTPS paths are open.
- Docker Engine and Compose installed from a maintained source and kept current.
WEBUI_SECRET_KEYis long, random, private, and preserved across recreation.- Open WebUI data is stored on a named or host-mounted volume and verified after restart.
- First administrator account created; public registration disabled unless required.
- Ollama is not publicly exposed; remote endpoint keys are least-privilege and protected.
- HTTPS works on the intended hostname, with HTTP redirected and streaming tested.
- Encrypted backups include the persistent volume and configuration; a restore test has passed.
- Disk, memory, swap, restarts, certificates, authentication failures, and backup jobs are monitored.
- Update and rollback procedures identify the image version and preserve the data volume.
- Tool execution, code pipelines, uploads, and connected credentials have been reviewed as untrusted-input surfaces.
Choosing the simplest architecture that meets your needs
For a personal assistant, start with Open WebUI on a patched VPS and a remote compatible endpoint. This minimizes model-related memory pressure while you learn the container, authentication, proxy, and backup workflow. If keeping prompts on infrastructure you control is a requirement, add Ollama only after sizing the server for the chosen model and expected context and concurrency.
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For a small gaming group, separate user accounts, disable public registration, and monitor usage before inviting more people. If several users need simultaneous long conversations, treat that as a capacity-planning problem rather than simply adding CPU: RAM, context length, embeddings, and model loading behavior may be the limiting factors.
A VPS deployment is successful when it remains private, recoverable, and predictable—not merely when a chat page opens. Build the security, persistence, monitoring, and restore path before calling the assistant production-ready.
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