Our Riyadh deployments follow a pattern refined across multiple Saudi engagements. We start with infrastructure assessment — evaluating whether the client's existing data center can support GPU workloads (most can with NVIDIA A100/H100 upgrades) or whether KSA-region cloud (AWS Bahrain, Azure UAE, or Oracle Jeddah) is the better path. For truly air-gapped environments — common in government and defense — we deploy fully offline model serving with vLLM or TGI on bare metal. The model selection phase is critical: we benchmark open-weight models (Llama 3, Mistral, Command R+, Jais for Arabic NLP) against the client's actual use cases, not synthetic benchmarks. Jais, developed by G42 in the UAE, is particularly relevant for Arabic-first applications — it outperforms multilingual models on Arabic text classification and generation by 15-20% in our testing. We handle the full MLOps stack: model quantization (GPTQ/AWQ for inference cost reduction), KV cache optimization, load balancing across GPU nodes, and monitoring with custom dashboards that track latency, throughput, and model drift.