Open-source LLM Self-Hosting (vLLM / Ollama)

Run powerful open-source models — Llama, Mistral, Qwen and similar — on infrastructure you control, instead of sending every prompt and document to a third-party API. The two big reasons UAE businesses do this are data residency (sensitive data never leaves your server, which matters for legal, healthcare, finance and government-adjacent work) and cost control (a flat infrastructure bill instead of unpredictable per-token charges at high volume). I select the right model and quantization for your accuracy and budget, size the GPU correctly, and serve it with vLLM for high-throughput production or Ollama for a simpler internal setup.

You get an OpenAI-compatible API endpoint, so apps and tools that already speak to OpenAI can point at your own server with a one-line base-URL change — no rewrite. I handle quantization to fit the model on sensible hardware, secure the API with auth and rate limits, set up basic autoscaling or a fallback, and load-test before handover. Deployment can sit on a UAE-region cloud or VPS to keep data in-country. Suited to teams in Dubai, Abu Dhabi and Sharjah that need privacy and predictable cost.

This is the deploy-and-serve service — distinct from my sibling LLM Fine-tuning & Training gig, which adapts a model's weights to your data and tone. Here I take open models as they are and stand them up reliably behind a private API for residency and cost; if you also want the model tuned on your data, that is the separate fine-tuning gig and the two pair well together.

How it works

1. 1
   Pick model + hardware

   We match a model and GPU to your accuracy, volume and budget

2. 2
   Deploy + secure the API

   I stand up vLLM/Ollama, expose an OpenAI-compatible secured endpoint

3. 3
   Load-test + handover

   We benchmark throughput, then hand over with a runbook and support