Benchmark
Arabic Language Model Performance Comparison
Detailed benchmark of leading Arabic-capable LLMs, covering MSA, Egyptian, Gulf, and Levantine dialects across telecom, banking, and government scenarios.
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Executive Summary
This analysis compares the total cost of ownership (TCO) for GPU infrastructure between cloud-based and on-premise deployments. Our findings indicate that for sustained AI workloads, on-premise deployment can deliver 40–60% cost savings over a 3–5 year period.
Methodology
Time horizons of 3 and 5 years
Training, inference, and mixed workloads
NVIDIA A100 / H100–class GPUs
Multiple utilisation scenarios (25–100%)
Cloud Cost Analysis
Instance costs on AWS, Azure, GCP
Storage and egress charges
Support and premium SLAs
On-Premise Cost Analysis
Hardware acquisition (e.g. DGX A100/H100)
Power, cooling, and space
Operations and maintenance
Depreciation and lifecycle
Key Findings
Break-even typically around 40–50% utilisation over 3 years
At high utilisation, on-premise can save hundreds of thousands of dollars
Data sovereignty and predictability are major non-financial advantages
Recommendations
Choose **on-prem** when:
Workloads are predictable and sustained
Data sovereignty is required
Long-term AI strategy is defined
Choose **cloud** when:
Workloads are highly variable
Projects are experimental or short-term
Rapid global scaling is required
Conclusion
For enterprises with sustained AI workloads and sovereignty requirements, on-premise GPU infrastructure often delivers superior economics and control compared to public cloud–only strategies.