Private LLM Installation Service
Run powerful AI language models entirely on your own server. Your data never leaves your infrastructure: complete privacy, complete control.
Get Started View GPU ServersSupported LLM Frameworks
We install and configure the leading open-source AI frameworks on your hardware.
Ollama
Easiest local LLM runtime. Supports LLaMA 3, Mistral, Phi, Gemma and more.
LLaMA 3
Meta's powerful open-source model, 8B to 70B parameters, optimised for your hardware.
Mistral
Efficient and fast. Ideal for enterprise use cases on CPU or GPU servers.
GPT4All
Runs fully offline on CPU. Great for low-resource servers and secure air-gapped environments.
What's Included
- LLM framework installation (Ollama, llama.cpp, etc.)
- GPU driver configuration (CUDA/ROCm) if applicable
- Model download and quantisation setup
- REST API endpoint configuration
- Open WebUI or similar chat interface setup
- Security hardening and firewall rules
- SSL/HTTPS configuration
- Handover documentation
How a private LLM project works
From first call to a working, secured AI endpoint on your own hardware.
- 1
Discovery
We agree the model family, who will use it, which documents it should read and whether it needs a GPU.
- 2
Server
Use your existing server, or order a Ucartz VPS for small CPU models or a GPU server for larger ones.
- 3
Install & secure
We install the runtime, the model and a chat interface or API, then add SSL, authentication and firewall rules.
- 4
Handover
You get documentation and admin access. After that, support is pay-as-you-go with no retainer.
Sizing guide
Rough starting points for quantised (4-bit) models. We confirm sizing during discovery.
7B–8B models
Run on CPU with 16 GB RAM for light use, or on a GPU with 8 GB+ VRAM for fast responses. Good for internal assistants and summaries.
13B–14B models
Need about 32 GB RAM on CPU or 12–16 GB of GPU memory. Better reasoning for support drafts and document Q&A.
70B-class models
Need roughly 40 GB+ of GPU memory. Suited to demanding reasoning, coding and multilingual work.
What teams use private LLMs for
Knowledge assistant
Answer staff questions from your manuals, policies and tickets using retrieval over your own documents (RAG).
Document processing
Summarise contracts, extract fields from invoices and classify emails without sending data to outside AI services.
Support drafting
Draft replies for your helpdesk agents, connected through n8n to your ticket system.
Developer tools
Private code assistant and API for internal tools, with no source code leaving your network.
Automation
Trigger AI steps from n8n workflows: tag leads, translate messages or route requests.
Air-gapped use
Offline deployments for organisations that cannot use public cloud AI.
Engagement and pricing
Installation is quoted after a free discovery call. These are the related running costs:
| Option | Price | Best for |
|---|---|---|
| KVM VPS (CPU models) | from $10/mo | Small 7B–8B models, testing and light internal use |
| GPU dedicated server | from $382.72/mo | Larger models and fast, multi-user inference |
| On-demand engineer | $8 / 15 min · $28 / hour | Model updates, new integrations and fixes after handover |
Every Ucartz server also includes Free Basic Managed Support for quick tasks of about 5 minutes.
Run Your Own Private AI Today
Our server experts will install and configure your chosen LLM, ready to use in under 24 hours.
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