1 min read

White Paper: GPU-Initiated, Liquid-Cooled, Ultra-High-Density Storage for Next-Gen AI

White Paper: GPU-Initiated, Liquid-Cooled, Ultra-High-Density Storage for Next-Gen AI
White Paper: GPU-Initiated, Liquid-Cooled, Ultra-High-Density Storage for Next-Gen AI
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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 combining NVIDIA SCADA (Scaled Accelerated Data Access) with a 100% liquid-cooled, fanless chassis housing 96 E3.S NVMe drives, we have created an ultra-high-density storage solution. 

Our innovative approach bypasses the CPU for the control path, allowing 100,000+ concurrent GPU threads to initiate their own data requests and approach a theoretical limit of 100 million IOPS. This fanless environment eliminates mechanical vibrations, ensuring stable tail latencies and protecting drive longevity, while optimizing power for compute rather than air movement. 

See how our GPU-initiated storage and advanced thermal management transform data centers by reducing physical footprints by up to 75% and lowering total Data Center OpEx by 40-50%. By streamlining data access for complex RAG, vector databases, and Graph Neural Networks (GNNs), we enable AI enterprises to minimize Time-To-First-Token (TTFT) and maximize GPU cluster utilization. Download the whitepaper to explore our system architecture and implement a silent, ultra-high-performance accelerator for your next-generation AI ecosystem. 

White Paper: GPU-Initiated, Liquid-Cooled, Ultra-High-Density Storage for Next-Gen AI

1 min read

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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White Paper: KV Cache Offload to Improve AI Inferencing Cost and Performance

1 min read

White Paper: KV Cache Offload to Improve AI Inferencing Cost and Performance

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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Autonomous AI Agent for End-to-End Component Data Extraction

1 min read

Autonomous AI Agent for End-to-End Component Data Extraction

This paper explores an advanced framework designed to automate the extraction of important attributes from unstructured part datasheets. By...

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