Let's get the disappointing part out of the way, because the internet is full of pages that don't.
Our servers are CPU-only. We don't offer GPUs. That means local LLM work here has a hard ceiling, and pretending otherwise would just waste your money.
What actually works on CPU
With up to 6 vCPU and 6 GB of RAM (our Medium plan — $12/mo), you're in the range of small quantized models:
- 1B–3B models (Q4): genuinely usable. Fast enough for classification, tagging, routing, and simple structured extraction.
- 7B–8B models (Q4): run, but expect a few tokens per second. Fine for a queue that chews through documents overnight. Painful as a chat window.
- Embedding models: excellent fit. They're small, fast on CPU, and this is probably the single best reason to run Ollama on a box like ours.
- 13B and up: don't. You'll be swapping and waiting.
If you need a fast, interactive 70B chat, you need a GPU host — that's a different product from a different provider, and we'd rather tell you than take your $12.
Where a CPU box genuinely wins
Privacy. Your documents, your prompts, your embeddings — never leaving a machine you control, never becoming someone's training data. For a lot of people that's the entire point, and a few tokens per second is an acceptable trade.
Cost predictability. No per-token bill. A pipeline that classifies fifty thousand records costs the same as one that classifies fifty: $12.
Async work. Most useful LLM work isn't a chat window. It's a queue: summarize these, tag those, embed this corpus. A CPU box grinding through a backlog overnight is perfectly good at that.
Setup
# Ubuntu 24.04 — Medium plan recommended
curl -fsSL https://ollama.com/install.sh | sh
# Start with something small and see for yourself
ollama pull llama3.2:3b
ollama run llama3.2:3b "Summarize this in one sentence: ..."
# Embeddings — the sweet spot for CPU
ollama pull nomic-embed-text
curl http://localhost:11434/api/embeddings \
-d '{"model":"nomic-embed-text","prompt":"hello world"}'
Ollama serves an HTTP API on localhost:11434. Keep it there. If you need to reach it from elsewhere, put it behind a reverse proxy with authentication on a dedicated-IP plan — never expose an unauthenticated model endpoint to the open internet.
Pair it with a vector database (Qdrant or pgvector in Docker) and you have a complete private RAG stack on one $12 box. That combination — small local embeddings, local vector store, external API only for the heavy generation step — is the setup that actually makes sense on hardware like this.
Picking a plan
| Use | Plan | Price |
|---|---|---|
| Embeddings, 1–3B models, RAG pipeline | Medium | $12/mo |
| Same, plus a public endpoint behind auth | Medium-IP | $20/mo |
| Just testing small models | Small | $8/mo |
CPU-only, up to 6 vCPU and 6 GB RAM. NVMe storage, unmetered traffic, Germany or Finland. Crypto payment, no KYC. Yearly billing is one transfer instead of twelve.
Related reading
- Host a vector DB for AI memory — the other half of a private RAG stack
- VPS for AI agents — orchestration on the same box
Ready? Deploy a server → — live in about a minute, paid in crypto, no ID required.