Host an LLM on a dedicated server: your AI, your data
Run your own large language model on a Kimsufi dedicated server. Reserved resources, full root access, ISO 27001 certified datacenters: your prompts and documents stay on your infrastructure. To start with small models, the processor is enough; for the largest ones, the RISE-GPU-1 provides a GPU.
Why host your own LLM?
An LLM (Large Language Model) is a deep learning model trained on huge volumes of text. It generates, summarizes, translates, classifies or extracts information in natural language. It is the engine behind chatbots, writing assistants, AI agents and search across your documents (RAG).
To use one, there are two routes: call a provider's API (OpenAI, Mistral AI, Anthropic…) or run an open source model locally, on your own machine. A cloud API remains the simplest way to get started and gives access to the most advanced models. Self-hosting takes over when one of these needs becomes a priority:
- Privacy: contracts, customer data, source code or HR files never leave your infrastructure. No prompt is sent to a third party.
- Predictable cost: an LLM hosted on your dedicated server costs the price of the machine, whatever the number of requests. Providers bill per token; heavy, regular usage quickly drives up the bill.
- Customization: you choose the model, its quantization, its context size and your system instructions, and you can adapt it to your data through fine-tuning or connect it to your document base.
- Stability: no quota imposed by a provider, no forced version changes. The model you validated remains the one that answers.
Kimsufi, the OVHcloud Eco range, provides unmanaged dedicated servers: you install and administer the inference engine, the model and the interface, keeping control over the entire chain.
Why choose a Kimsufi dedicated server to host an LLM?
Reserved resources
Processor, RAM and storage are not shared with anyone: your model's generation speed does not depend on other customers' workloads.
Full root access
Install Ollama, vLLM or the engine of your choice, select your models and tune every inference parameter. Nothing is imposed, from the Linux distribution to the model format.
Data under control
ISO 27001 certified datacenters and Anti-DDoS protection included with all offers: your prompts and documents stay on a machine you administer.
Controlled costs
Unmetered traffic is included and you do not pay per token: the cost of your LLM does not climb with the number of users or requests.
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Use cases: what a self-hosted LLM brings to your team
An LLM hosted on your own server rarely serves just one project. A few usage examples:
- Internal assistant: Open WebUI lets you give your teams a private chat, similar to ChatGPT, without any conversation leaving the company.
- Document search (RAG): the LLM answers from your knowledge base, procedures or contracts, citing its sources. A vector database such as Qdrant or pgvector indexes the documents.
- Batch processing: classify tickets, extract fields from invoices, summarize meeting notes or anonymize data, overnight, with no response-time constraint.
- Coding assistant: a tool such as the Continue extension connects to your local endpoint and helps your developers without sending their code to an external service.
- Agents and automation: your n8n workflows or your agents call the local LLM to sort, write or decide, instead of an API billed per call.
A single server can host several of these services, provided you size memory capacity and compute for all of them.
Cloud API, hourly GPU or dedicated server: which hosting for your LLM?
Four main LLM hosting options coexist, each suited to a particular use:
- A provider's API: no infrastructure to manage, the most powerful models, per-token billing. Ideal for prototyping a project or for low volumes, less suited to sensitive data and intensive use.
- Hourly cloud GPU: GPU instances rented on demand, useful for training or a one-off compute peak. The cost varies with usage time, and the setup has to be redone at each session if you keep nothing.
- A dedicated server: an entire machine on which your LLM runs permanently, with a fixed cost and control over your data.
- On-premise: your own hardware on your premises. Maximum control, but hardware purchase, electricity, cooling and maintenance are your responsibility.
A dedicated server sits between the two extremes: the control of on-premise, without the hardware investment or the management of a server room. It is a relevant option for an LLM used every day by a team or by an application in production, and the natural entry point to self-hosting when the public cloud no longer fits.
What is an LLM in AI?
An LLM (Large Language Model) is a deep learning model trained on huge volumes of text to understand and generate natural language. It powers chatbots, assisted writing, translation, document analysis and AI agents.
How do I host my own AI?
Choose an infrastructure (CPU or GPU dedicated server), install an inference engine such as Ollama or vLLM, download the model, expose a secure API and monitor resources. Also plan for backups and regular updates.
Can you host an LLM without a GPU?
Yes. Small quantized models run on CPU with Ollama, with slower generation than with a graphics card. This suits tests, lightly used internal assistants and batch processing. For large models or many simultaneous users, a GPU server such as the RISE-GPU-1 is required.
What is the difference between an LLM and AI?
Artificial intelligence covers all the techniques that enable a machine to solve problems: vision, recommendation, robotics, language. An LLM is a subcategory of AI, specialized in natural language processing.
Which open source LLM should you choose?
It all depends on your project, the language and your hardware. Llama, Mistral, Qwen and Gemma are among the most widespread families for self-hosting; Mistral AI, a French publisher, releases open-weight models. Test two or three candidates on your own data before choosing.
How much does LLM hosting cost?
With an open source model hosted on your own infrastructure, there is no per-token billing: you pay for the infrastructure. The price of a Kimsufi server depends on the range (KS, SYS, RISE or RISE-GPU-1), the configuration and the country; it is displayed on each offer. Unmetered traffic is included.