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[ARTICLE · art-106456] src=gist.github.com ↗ pub= topic=developer-tools verified=true sentiment=· neutral

Self-hosted AI assistant on a $9 VPS — Ollama + Open WebUI + Caddy

A developer has documented a self-hosted AI assistant setup running Ollama, Open WebUI, and Caddy in Docker on a $9 Hostinger KVM2 VPS with 2 vCPUs, 8 GB RAM, and no GPU. The setup achieves token generation speeds of about 16 tokens/sec for the llama3.2:1b model and 13 tokens/sec for llama3.2:3b, while qwen2.5:7b fits but is not recommended due to tight memory. The developer advises hardening the server first, disabling signups after creating an admin account, and turning off Open WebUI's auto-generation features to reduce CPU load.

read1 min views1 publishedAug 21, 2026

Ollama + Open WebUI + Caddy, running in Docker on a Hostinger KVM2 (2 vCPU, 8 GB RAM, 100 GB NVMe, Ubuntu 24.04).

Video: [https://youtu.be/-OlX98vV8Sg](https://youtu.be/-OlX98vV8Sg)
VPS: [https://hostinger.com/daniellaera](https://hostinger.com/daniellaera) — code `DANIELLAERA`

for 10% off
Model Tokens/sec
llama3.2:1b ~16
llama3.2:3b ~13
qwen2.5:7b fits in 8 GB, but very tight — not recommended

Short prompts (a commit message, a quick explanation) come back in a few seconds. Long generations take a minute or more. There is no GPU on this plan — everything runs on CPU.

01-harden.sh

— do this first, before anything else02-docker.sh

compose.yml

+Caddyfile

docker compose up -d

  • Pull a model, create your admin account, then disable signups

  • Set a system prompt like "Answer directly, no preamble, be brief"— a short answer is a fast answer. - In Open WebUI, Admin Panel → Settings → Interface: turn off Title Auto-Generation, Follow-Up Generation, Tags Generation and Autocomplete. Each one fires an extra hidden generation on every message and they all compete for the same two cores. OLLAMA_KEEP_ALIVE

keeps the model resident in RAM. Longer means no cold-load delay; shorter frees the memory when idle.

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