LLM VPS — Run Ollama & AI Models on Your Server

Self-host LLM inference with Ollama on an EU VPS: Ryzen 9 7950X3D, up to 96 GB RAM, full root, no per-token fees. Guide included, from €13/mo, pay with crypto.

What runs well on a CPU VPS

Honest sizing first: this is a CPU server, not an GPU cloud. On Ryzen 9 7950X3D with fast NVMe, quantized 7B–14B models (Q4/Q5) generate usable tokens for chat, classification, extraction and tool-calling agents. That covers most internal tools. If you need hundreds of tokens per second on 70B models — that is GPU territory and we will say so plainly.

Plan picks by model size

WorkloadPlanFit
Experiments, embeddings, small chatCore VPS 4 NVMe — €13/mo8 GB RAM: 7B Q4 models comfortably
Production agent, tools, RAGCore VPS 12 NVMe — €60/mo48 GB RAM: 14B Q5 + vector DB side by side
Big context, batch jobs, many modelsPerformance Plus 16 — €165/mo64 GB RAM and 16 vCPU for parallel prompts

Stack in four commands

  1. Deploy NVMe plan from €13, paid by crypto or card.
  2. Install Ollama The Ollama guide walks through install, model pull, API bind and systemd.
  3. Expose safely Bind to localhost, put your app or a reverse proxy with auth in front — never the raw API on the internet.

Quick answers

Do you have GPU servers?

No — and we do not pretend otherwise. For GPU inference use a GPU provider; for CPU-friendly models, private data and predictable costs, this is the better tool.

Is the API rate-limited?

No — it is your localhost. Rate limits only exist if you put a proxy in front of it yourself.

How do model updates work?

`ollama pull` when you want a new tag; keep the old tag until you are happy — disk is the only cost.

Inference without invoices Flat monthly price, full root, EU datacenter. Deploy for AI →

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