Giga Computing Launches New Power-Efficient Hardware for AI Data Centers
技嘉科技推出用於 AI 資料中心的新型節能硬體
The rapid rise of generative AI has led to the industrialization of AI, where data centers must evolve into "AI Factories."
生成式AI的迅速崛起帶動了AI的產業化，數據中心必須演進為「AI工廠」。
Giga Computing, a subsidiary of GIGABYTE, is leading this shift by moving away from individual server management to a rack-scale orchestration strategy.
技嘉科技旗下子公司技嘉運算（Giga Computing）正引領這一轉變，從個別伺服器管理轉向機櫃級編排策略。
As modern AI workloads demand massive power, often exceeding 50-100kW per rack, Giga Computing provides comprehensive infrastructure to handle these requirements.
由於現代AI工作負載需求極大的功率，每機櫃通常超過50-100kW，Giga Computing提供全面的基礎設施以應對這些要求。
Their flagship GIGAPOD solution aggregates hundreds of GPUs into a single, modular cluster designed for peak efficiency.
其旗艦級GIGAPOD解決方案將數百個GPU集成至單一模組化叢集，旨在實現最高效率。
To manage the intense heat generated by such high-density hardware, they utilize Direct Liquid Cooling (DLC), which significantly lowers energy costs compared to traditional air cooling.
為了管理這類高密度硬體所產生的強烈熱能，他們採用直接液冷（DLC）技術，與傳統空冷相比，該技術顯著降低了能源成本。
The integration is managed by the GIGABYTE POD Manager, which acts as the system's brain for predictive analytics and resource allocation.
此整合過程由GIGABYTE POD Manager管理，它充當系統大腦，用於預測分析和資源分配。
By offering turnkey L12-level services—ranging from facility design to system integration—Giga Computing is simplifying the logistical complexity of building high-performance data centers.
透過提供包含從設施設計到系統整合的L12級統包式服務，Giga Computing正在簡化建置高性能數據中心的後勤複雜性。
Through initiatives like the GAIFA accelerator, they are proving that the future of computing depends not just on single-chip speed, but on the integrated throughput of entire data center ecosystems.
透過GAIFA加速器等計畫，他們證明了運算的未來不僅取決於單一晶片的速度，更取決於整體數據中心生態系統的綜合吞吐量。
