How much RAM does Ollama need?

A 7B parameter model in Q4 quantization requires roughly 5–6 GB of RAM. Plan for 8 GB minimum to run a 7B/8B model comfortably. For 13B models, 16 GB; for 70B models (slow on CPU), 64 GB or a GPU is recommended.

Does Ollama work without a GPU?

Yes. CPU-only inference works out of the box. Smaller quantized models (Q4_K_M) run at usable speeds on modern CPUs for dev or low-volume use. For production latency on 7B+ models, a GPU accelerates inference 10–30×.

Can I pair Ollama with Open WebUI?

Yes — they are designed to work together. Open WebUI auto-detects a local Ollama instance and lists all pulled models in its model selector. Both can run on the same VPS; Open WebUI adds a full chat UI, user management, and RAG on top of Ollama's inference engine.

Build, host and run AI solutions.

Ollama

Run open-source LLMs on your VPS — OpenAI-compatible API, zero per-token cost.

8 GB minimum (16 GB recommended) RAM 2 vCPU Port 11434 Available

Source code

Tech stack

DockerGollama.cpp

Minimum RAM8 GB minimum (16 GB recommended)

Minimum CPU2 vCPU

Default port11434

Compatible OSubuntu-24.04

Ollama is an open-source LLM runtime that lets you pull and serve large language models (Llama 3, Mistral, Qwen, DeepSeek, Gemma) on your own infrastructure with a single command. It exposes an OpenAI-compatible REST API on port 11434, so any tool or library that works with OpenAI also works with your self-hosted instance — without changing a single line of code.

Deployed on a ServOrbit VPS, Ollama becomes your private AI inference backend: unlimited requests at a fixed monthly cost, with all prompts and responses staying on your server. It pairs directly with Open WebUI for a chat interface, Flowise or n8n for automation, or any custom application that calls `/v1/chat/completions`.

Key features

OpenAI-compatible API — swap the base URL and your existing code works instantly

One-command model management: ollama pull llama3.1:8b, ollama list, ollama rm

Supports Llama 3, Mistral, Qwen, DeepSeek, Gemma, Phi and 100+ other models

CPU-only mode works out of the box; optional GPU passthrough for faster inference

Multi-model server: load several models concurrently, each on its own port or context

Persistent model storage in a Docker volume — models survive container restarts

When to use this solution?

1

Private AI backend

Replace OpenAI API calls with your own Ollama endpoint. Your prompts never leave your VPS — ideal for processing contracts, internal documents, or any sensitive data.

2

Local dev & testing

Run a 7B or 13B model on a dedicated VPS to prototype AI features before wiring up a production LLM. Fixed cost, no surprise bills, reset with ollama rm.

3

Shared team AI stack

Pair Ollama with Open WebUI to give your whole team a shared ChatGPT-like interface powered by open-source models — on infrastructure you control.

Deploy Ollama on your VPS

Guide optimized for ServOrbit Cloud VPS.

01

Create the VPS

Order a ServOrbit VPS with at least 8 GB of RAM for a 7 to 8 billion parameter model quantised to Q4 on CPU, 16 GB for larger models. The system installed is Ubuntu 24.04. For production-grade response times, a server fitted with a GPU changes the experience markedly.

02

Deploy Ollama

Installing from your client area creates the ollama/ollama container, mounts the ollama volume on /root/.ollama so your models survive reboots, and publishes the service on the loopback interface, at 127.0.0.1:11434. Nothing is exposed to the internet at this stage: that is deliberate, since Ollama has no authentication of any kind.

03

Connect for the first time

Ollama has no sign-in screen: it is an HTTP API paired with a command line. Over SSH on the VPS, run docker exec -it ollama ollama pull llama3 then docker exec -it ollama ollama run llama3. Then plug in an interface — Open WebUI for instance — rather than exposing the API as it stands.

04

Pull your first model

Fetch a model with docker exec -it ollama ollama pull llama3.1:8b, check that it is registered with docker exec -it ollama ollama list, then test from the VPS: curl http://127.0.0.1:11434/api/generate -d '{"model":"llama3.1:8b","prompt":"Hello"}'.

05

Account for the lack of authentication

Ollama asks for no credentials and knows nothing of API keys: anyone who reaches the service can query your models and pull new ones. As soon as a domain is attached, ServOrbit installs the nginx vhost and the TLS certificate — but encryption protects the transport, it identifies nobody. Two options hold up: leave the instance without a domain and work through an SSH tunnel, or put an authenticating gateway in front of Ollama — LiteLLM for instance — and publish only that.

06

Connect your tools

In Open WebUI, open Settings then Connections and enter http://localhost:11434. From an OpenAI SDK running on the VPS itself, set base_url='http://127.0.0.1:11434/v1' and api_key='ollama'. From any other machine, go through the domain attached to the instance — base_url='https://<your-domain>/v1'. Without a domain there is no public entry point: open an SSH tunnel with ssh -L 11434:127.0.0.1:11434 root@<your-vps-ip> then point at http://localhost:11434/v1. The port may be reassigned at install time: use the one shown on the app's card in your client area, 11434 being only the catalogue value.

Frequently asked questions

Ollama is an open-source runtime that downloads and serves large language models (LLMs) via a local REST API. Think of it as a local inference server: you pull a model once, and any application can call it just like it would call the OpenAI API.

Yes. Ollama exposes an OpenAI-compatible endpoint at /v1/chat/completions, /v1/completions, and /v1/embeddings. Change base_url to your Ollama host and set api_key to any non-empty string — existing code requires no other changes.

Run docker exec -it ollama ollama pull <model-name> from your VPS. Browse available models at ollama.com/library. Popular choices: llama3.1:8b, mistral:7b, qwen2.5:7b, deepseek-r1:7b, gemma2:9b.

Embed the deploy button

Maintaining a project that uses Ollama? This button lets your readers deploy it on a VPS in one click, without reading Docker documentation.

Markdown

[![Deploy Ollama on ServOrbit](https://servorbit.com/brand/deploy/button.svg)](https://servorbit.com/vps-cloud?template=ollama&utm_source=deploy-badge&utm_medium=referral&utm_campaign=ollama)

HTML

<a href="https://servorbit.com/vps-cloud?template=ollama&utm_source=deploy-badge&utm_medium=referral&utm_campaign=ollama"><img src="https://servorbit.com/brand/deploy/button.svg" alt="Deploy Ollama on ServOrbit" height="40"></a>

The button points to a VPS order with the template preselected. The image is served from servorbit.com — nothing to host on your side.

Complementary solutions

Artificial Intelligence

Activate Ollama on your infrastructure.

Dedicated Cloud VPS — IPv4 included, European datacenter, support included. Your data never leaves your server.

Recommended configuration: 8 GB minimum (16 GB recommended) RAM · 2 vCPU