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White Paper: GPU-Initiated, Liquid-Cooled, Ultra-High-Density Storage for Next-Gen AI
This paper introduces a paradigm shift in storage architecture designed to overcome the CPU-centric data path bottlenecks in modern AI workloads. By...
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Updated on July 11, 2025
The integration of advanced liquid cooling systems in AI clusters is essential for maintaining thermal stability and optimizing performance. Key components such as AI racks, Cooling Distribution Units (CDUs), the Wiwynn UMS100 (Universal Management System), and AMI DCM (Data Center Manager) work together to ensure efficient cooling management. The Wiwynn UMS100 manages cooling at multiple levels, serving as a bridge between individual servers and the overall cooling system, while the AMI DCM provides a centralized platform for monitoring and managing all servers and devices within the AI cluster. This integrated approach enhances performance, improves energy efficiency, and ensures reliability.
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This paper introduces a paradigm shift in storage architecture designed to overcome the CPU-centric data path bottlenecks in modern AI workloads. By...
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This paper explores a disaggregated key-value (KV) storage architecture designed to efficiently offload KV cache tensors for generative AI workloads.
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This paper explores an advanced framework designed to automate the extraction of important attributes from unstructured part datasheets. By...