اختبار مقارن
مقارنة أداء نماذج اللغة العربية
اختبار تفصيلي لأداء النماذج اللغوية الداعمة للعربية في الفصحى واللهجات المصرية والخليجية والشامية عبر سيناريوهات الاتصالات والبنوك والجهات الحكومية.
قراءة المزيد
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.