Self-hosted open models
Open-weight LLMs running on your own servers
We deploy open-weight models such as Llama, Qwen and Mistral on your hardware or private cloud, tuned for Arabic. Documents and prompts never leave your network, and costs stay fixed as usage grows.
Discuss self-hosted open models for your teamWhat we deliver
From sizing the server to serving the model.
- 01Hardware sizing and GPU server setup
- 02Model selection and Arabic evaluation
- 03Fine-tuning on your own data
- 04High-throughput model serving
- 05Role-based access and audit trail
- 06Ongoing monitoring and model updates
Where it fits
Asked often.
No. When an open model such as Llama, Qwen or Mistral runs on your own servers or private cloud, documents and prompts stay inside your network and nothing is sent to an outside AI provider. Where your data lives then depends only on where your servers or private cloud region are located. That makes it the usual choice for legal, financial, government and regulated work, with an audit trail and role-based access your own team controls.
For many focused business tasks they are close enough, but the strongest commercial models still lead on complex reasoning and broad general knowledge. Open models do well at answering from documents, extraction and summarisation, especially after evaluation and fine-tuning on your own data. We test candidate models in Arabic on your real questions during the pilot, so you see measured accuracy before deciding. If the gap matters for your use case, a commercial API or a private cloud tenant may be the better fit.
It depends on the model size, the number of users and how fast answers need to be, so sizing comes first. Most deployments need one or more GPU servers, either bought for your data centre or rented in a private cloud. We size the hardware from your expected usage, then set up model serving, access control and monitoring. Costs stay fixed as usage grows, which suits steady, high volume work better than occasional use.
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