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المصطلحات التقنية

استدلال الذكاء الاصطناعي

مرحلة تشغيل النموذج لإنتاج مخرجات من مدخلات جديدة، وتختلف عن مرحلة التدريب من حيث أهداف الأداء والتشغيل.

التنفيذ متعدد الأجهزة

تقسيم تنفيذ عبء العمل عبر أكثر من جهاز حوسبة بدلاً من الاعتماد على جهاز منفرد.

Source facts referenced from NVIDIA: https://developer.nvidia.com/blog/scaling-ai-inference-across-multiple-gpus-using-nvidia-tensorrt-with-multi-device-inference-support. This article is an original Kenzie synthesis and does not reproduce the source article.

Verified source facts used: NVIDIA is the publisher; the official URL is identified; the supplied title concerns scaling AI inference across multiple GPUs using a named NVIDIA inference runtime with multi-device support; the summary states that generative AI workloads are exceeding single-device memory and compute limits; it identifies media-generation inference developers as an affected audience; and it names preservation of production optimizations such as kernel fusion, memory planning, and quantization as the central challenge. Evidence limits: only the RSS title and summary were treated as verified; no benchmark, architecture diagram, API behavior, implementation step, compatibility matrix, date-based product status, security claim, cost result, or regional impact was used. Claims deliberately not made: no assertion of measured performance, deployment recommendation, legal conclusion, Saudi or GCC relevance, product superiority, or guaranteed operational outcome. Independent decision reasoning added: the article converts the stated engineering tension into enterprise evaluation questions about governance, validation ownership, and optimization trade-offs without attributing those principles to the source. Automated copyright score: 70. Source-overlap ratio: 0.0137. Longest source match: 14 words. Rights basis: trusted syndicated RSS metadata used only for factual, attributed synthesis.