Self-hosted LLMs
Open-weight models such as LLaMA, Qwen and Mistral, served with vLLM or Ollama behind an OpenAI-compatible API, so internal apps can move off a cloud API with little code change.
Services · On-premise AI
On-premise LLM deployment in Saudi Arabia
Many organizations in Saudi Arabia can't send documents, recordings or customer data to an external AI service. Government entities, banks, healthcare providers and large enterprises often need data to stay inside the Kingdom and inside their own network. I deploy open-weight language models on infrastructure you control, so your teams get generative AI without that data leaving it.
I've led on-premise deployments of LLaMA and Qwen models on GPU Kubernetes for large Saudi enterprises, with RAG assistants and Whisper speech-to-text running on the same platform.
What I deploy
Private RAG
Retrieval over FAISS or Chroma inside your network, connected to internal documents and systems, with access control for each user.
Speech-to-text
Whisper large-v3 transcription of calls, meetings and recordings on your own GPUs, feeding information extraction, search and analytics.
Private fine-tuning
LoRA and QLoRA fine-tuning on your data and your hardware, when a base model needs your terminology, formats or tasks.
How a deployment runs
Requirements and sizing
Use cases, number of users, response-time targets and data classification. From those I size the models and GPUs, and decide whether a single server is enough or a Kubernetes cluster is needed.
Model selection
Candidate models are tested on your data, including Arabic if your users need it. Quantization and batching are tuned so the chosen model fits your hardware and budget.
Deployment
Containers on Kubernetes or a GPU server, vLLM serving, authentication, logging and integration with your identity and document systems. Offline installation is possible for networks with no internet access.
Operations and handover
Monitoring of latency, GPU usage and answer quality; model updates through CI/CD with GitHub Actions and MLflow; and documentation and training so your team can run it.
Work this is based on
On-premise LLM and speech platform
Enterprise RAG assistants, LLM chatbots and Whisper large-v3 transcription on self-hosted LLaMA and Qwen models on GPU Kubernetes, for large-scale Saudi enterprise use cases. Retrieval runs over FAISS and Chroma, and transcripts feed NLP extraction and analytics, all without data leaving the client's infrastructure.
Impact: data-sovereign generative AI for privacy-critical environments.
MLOps for model delivery
End-to-end pipelines with MLflow, Weights & Biases and GitHub Actions CI/CD for experiment tracking, reliable deployments and faster model releases.
AI meeting assistant
Whisper transcription, LLM summaries and LangChain agents that extract action items and draft follow-ups for enterprise teams.
Impact: post-meeting documentation cut from hours to minutes.
On-premise, in-Kingdom cloud or external API?
Saudi Arabia's Personal Data Protection Law (PDPL) and sector regulators set rules on where personal data can be stored and processed. Your legal and compliance teams decide what applies to you; I build the system to meet it.
On-premise
Best when data can't leave your network, usage is steady and heavy, or you need full control over models and updates. You provide or buy the GPU servers.
Cloud region in Saudi Arabia
Data stays in the Kingdom without running your own hardware. Good for pilots and changing workloads, if your policies allow a cloud provider.
External API
The fastest start and the strongest frontier models, for data that is allowed to leave. Often a good way to build a prototype before moving on-premise.
Tools I use
Common questions
What hardware do we need?
It depends on model size, number of users and the response time you need. A pilot can run on a single GPU server; production usually runs on a small GPU cluster. I size it from your requirements before you buy anything.
Are open-weight models good enough?
For most enterprise assistants and search, yes, especially with RAG over your own documents. I test them on your tasks next to an API model, so you see the difference before you decide.
Does it support Arabic?
Yes. Multilingual open-weight models handle Arabic, and I evaluate them on Arabic test sets from your own domain.
How do we start?
Usually with a short consultation to review your use case and infrastructure, then a pilot on a single GPU server with your own data.
Need AI that stays inside your network?
Tell me about your use case, your users and your infrastructure. I reply in English or Arabic.