Guide
On-premise LLMs: private AI models for businesses
Guide to on-premise Large Language Models for businesses. Llama, Mistral, DeepSeek, Qwen, GLM, Kimi: how to choose and deploy private AI models in your infrastructure.
Why on-premise LLMs
Large Language Models (LLMs) are the engine of generative AI. When you use ChatGPT, you’re using an LLM — but your data travels to American servers. On-premise LLMs offer the same power, with data remaining under your control.
Open-source models in 2026
The open-source AI model landscape has exploded. Here are the main players:
| Model | Developer | Strengths | Parameters |
|---|---|---|---|
| Llama 3 | Meta | General purpose, multilingual | 8B, 70B, 405B |
| Mistral | Mistral AI | Efficiency, European languages | 7B, 22B, 123B |
| DeepSeek R1 | DeepSeek | Reasoning, coding | 7B, 67B, 671B |
| Qwen 3.5 | Alibaba | Multimodal, multilingual, reasoning | 7B, 72B, 235B |
| GLM 5 | Zhipu AI | Advanced reasoning, coding, multilingual | 9B, 32B |
| Kimi 2.5 | Moonshot AI | Long context, reasoning, agents | 70B+ |
| Gemma 2 | Compact, efficient | 2B, 9B, 27B |
Competition among open-source models has intensified enormously: Qwen 3.5, GLM 5 and Kimi 2.5 have demonstrated competitive performance with the best proprietary models, expanding the options for businesses that want private AI without compromising on quality.
On-premise vs cloud: the comparison
| Aspect | On-premise LLM | Cloud LLM (ChatGPT, Claude) |
|---|---|---|
| Data privacy | Total | Data on third-party servers |
| GDPR | Compliant by design | Requires DPA and safeguards |
| Cost | Fixed (hardware + software) | Variable (per token/user) |
| Latency | Low (local network) | Depends on connection |
| Customisation | Full (fine-tuning, RAG) | Limited |
| Vendor lock-in | None | High |
| Updates | Company’s choice | Unilateral from provider |
How ORCA works
Which model to choose, which version to use, when to update, how to configure: these are technical complexities that shouldn’t fall on someone running a company. That’s why ORCA exists: a solution that handles everything transparently — selects the best model for each need, keeps it up to date, ensures compliance with European regulations. The entrepreneur uses AI, not manages it.
ORCA is HT-X’s platform that simplifies on-premise LLM adoption:
- Installation: HT-X installs ORCA on company servers or a European private cloud
- Model configuration: selection and optimisation of the best models for each use case
- Knowledge base: connection to company documents and data (RAG)
- User interface: familiar chat for all employees, no technical training needed
- Updates: new models and features when the company decides
Business use cases
On-premise LLMs excel at:
- Document analysis: upload contracts, reports, manuals and get immediate answers
- Text generation: emails, reports, technical documentation
- Customer support: internal and external chatbots with company data
- Coding assistant: programming support with proprietary code
- Knowledge management: quick access to distributed corporate knowledge
Getting started with on-premise LLMs
The typical journey with HT-X:
- Assessment: analysis of requirements and existing infrastructure
- Proof of concept: testing with company data in 2-4 weeks
- Deployment: production installation and configuration
- Training: end-user training
- Support: ongoing assistance and updates
Frequently asked questions
On-premise LLMs (Large Language Models) are AI models installed directly on company servers, rather than used through cloud services. This ensures data never leaves the company infrastructure, providing total privacy and GDPR compliance.
The leading open-source models in 2026 are: Llama 3 (Meta) for general use, Mistral for efficiency and European language performance, DeepSeek for advanced reasoning, Qwen 3.5 (Alibaba) for multimodal and multilingual tasks, GLM 5 (Zhipu AI) for reasoning and coding, and Kimi 2.5 (Moonshot AI) for long-context tasks. ORCA supports all these models.
It depends on the model and number of users. For an SME with 10-50 users, a server with an NVIDIA A100 GPU or equivalent is sufficient for 7-13B parameter models. For larger models (70B+), multi-GPU configurations are needed. HT-X sizes hardware based on specific requirements.
Modern open-source models (Llama 3, Mistral, DeepSeek, Qwen 3.5) achieve performance comparable to GPT-4 in most business tasks. For activities like document analysis, text generation, customer support and coding, differences are minimal. The advantage is total data privacy.
Looking for a private ChatGPT for your business?
ORCA is the on-premise AI platform by HT-X (Human Technology eXcellence): your data stays yours, GDPR and AI Act compliant.
Discover ORCA