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AI / LLM VPS

VPS for AI and LLM Workloads (CPU-Optimised)

A VPS for AI is a KVM server with dedicated CPU, RAM and NVMe storage where you self-host AI workloads instead of paying per token. X-ZoneServers VPS are CPU-only, so they suit small quantised models, embeddings, RAG orchestration, vector databases and API gateways to hosted models. We have no GPU hardware, so GPU training and large-model real-time inference are out of scope.

Our AI VPS run on KVM virtualisation with guaranteed RAM, CPU cores and NVMe SSD per instance, which matters because CPU LLM inference is bound by memory bandwidth and RAM headroom, not GPU. A 7B-8B model quantised to Q4 GGUF typically needs around 16 GB RAM to load and serve comfortably; 2B-3B models and embedding models fit in 4-8 GB. Every plan includes 1 Gbps unmetered bandwidth, up to 1 Tbps of DDoS mitigation, full root access, a 99.9% uptime SLA and deployment in under 60 seconds across 12 datacentres in Europe and North America.

Deploy a VPSTalk to our team
< 60s
Deployment time
1 Gbps
Unmetered network
12
Global locations
Up to 1 Tbps
DDoS protection

Why it works

Infrastructure matched to the workload — dedicated resources, not a generic box.

CPU-only, honestly scoped

No GPU hardware, so we point you at what runs well on CPU: small quantised models, embeddings, classification and RAG, not GPU training or large-model real-time inference.

Dedicated RAM and NVMe

KVM gives each VPS guaranteed RAM, CPU and NVMe SSD. RAM headroom is the real constraint for CPU LLM inference, and we never oversubscribe your memory.

Self-host Ollama and llama.cpp

Full root access on Ubuntu, Debian, AlmaLinux or Rocky Linux lets you run Ollama or llama.cpp serving 3B-8B GGUF models with an OpenAI-compatible local API.

RAG and vector DB ready

Host Qdrant, Weaviate or Postgres with pgvector as a private RAG backend, plus Redis and your orchestration layer, on the same NVMe-backed instance.

AI gateway and automation

Run an AI API gateway or router in front of hosted models, and automate agent pipelines with n8n, Flowise, LangChain or LlamaIndex behind a stable HTTP endpoint.

Hourly billing, capped

Pay from EUR 0.0056/hour and spin servers up only when a job runs. Cost is capped at the monthly price, so a VPS running 24/7 never exceeds the listed plan.

Ideal for

These servers fit AI builders who want data ownership, a stable HTTP API and no per-request rate limits. Run Ollama or llama.cpp for small open models, host Qdrant, Weaviate or Postgres with pgvector as a RAG backend, and orchestrate pipelines with n8n, Flowise, LangChain or LlamaIndex. Many teams use a VPS as an AI API gateway or router in front of hosted models from OpenAI or Anthropic. Be realistic on speed: expect single-digit to low-double-digit tokens per second on CPU, ideal for batch and asynchronous work. GPU training and fine-tuning are out of scope on our CPU-only fleet.

  • Self-hosting Ollama or llama.cpp for small quantised 3B-8B models
  • RAG backends with Qdrant, Weaviate or pgvector
  • Embedding and document-classification batch jobs
  • AI API gateways and routers to hosted models
  • n8n and Flowise AI automation pipelines
  • Chatbot and agent backends behind a private API

Frequently asked questions

Can you run AI or an LLM on a VPS without a GPU?
Yes, for the right workloads. Small and quantised open models run on CPU: 2B-3B models in 4-8 GB RAM and 7B-8B Q4 GGUF models in around 16 GB. Embeddings, classification, summarisation and RAG orchestration all work well. GPU training and large-model real-time inference do not run on CPU and are out of scope here.
How much RAM do I need to host an LLM on a VPS?
RAM is the binding constraint for CPU inference. Plan for roughly the quantised model size plus headroom for the OS and serving process: about 4-8 GB for 2B-3B models, and around 16 GB for a 7B-8B model in Q4 GGUF. Vector databases and embedding indexes need additional RAM on top of the model.
Can I run Ollama on an X-ZoneServers VPS?
Yes. With full root access on a Linux VPS you can install Ollama or llama.cpp and serve GGUF models through a local OpenAI-compatible API on port 11434. Stick to small quantised models sized to your RAM. Expect single-digit to low-double-digit tokens per second, which suits batch and asynchronous tasks.
How fast is CPU LLM inference compared to a GPU?
Slower, and that is the honest trade-off. On CPU you typically see a few to around a dozen tokens per second, well below GPU throughput. That is fine for summarising, extracting, classifying, embeddings and overnight batch jobs, but not for high-throughput interactive chat. For real-time chat, put a hosted model behind an AI gateway running on your VPS.
Can I host a RAG backend or vector database on these VPS?
Yes. NVMe SSD and dedicated RAM make these servers a good fit for self-hosted vector databases such as Qdrant, Weaviate or Postgres with pgvector, alongside Redis and an orchestration layer like n8n, LangChain or LlamaIndex. A 4 vCPU / 16 GB / NVMe instance covers most early-stage RAG deployments.
Do you offer GPU servers for training or fine-tuning?
No. X-ZoneServers has no GPU hardware, so GPU training, fine-tuning at scale and large-model real-time inference are out of scope. Our VPS are best for CPU-appropriate AI: small models, embeddings, RAG, automation and acting as a gateway to hosted models. For heavier compute, see our dedicated servers.

Related products & use cases

VPS Hosting
Dedicated Servers
Docker VPS
Self-Hosting VPS

Deploy an AI VPS in under 60 seconds

Spin up a CPU-optimised KVM VPS for Ollama, RAG and AI automation. Hourly billing capped at the monthly price, with NVMe and DDoS protection included.

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