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SKU:GX10-GG0010BN
The Asus Ascent GX10-GG0010BN is a powerful desktop AI computer designed for AI development, data science, and high-performance computing. Powered by the NVIDIA GB10 Grace Blackwell Superchip and NVIDIA DGX™ Spark architecture, this compact device delivers petaflop-scale AI processing capabilities directly on your desktop, making it ideal for AI researchers, developers, and data scientists seeking reliable, high-speed performance. Its small form factor allows seamless integration into existing workspaces without sacrificing computational power, providing an efficient solution for demanding AI workloads.
The system features 128 GB of high-speed memory, enabling users to run multiple intensive applications simultaneously without lag. Its 1 TB PCIe NVMe 4.0 SSD ensures rapid data access, quick loading times, and ample storage for datasets, media libraries, and essential files. Built on Ubuntu LTS with DGX OS, the computer includes integrated tools like Base Command and NGC CLI for streamlined system management, workload orchestration, and software deployment. The NVIDIA Blackwell GPU GB10 graphics card enhances visual performance for gaming, graphic design, and visualization tasks, supporting high-frame-rate rendering and precise visual outputs.
Designed for reliability and security, the Asus Ascent GX10 incorporates ECC-protected caches, cryptographic extensions, and embedded profiling tools. Its features include a security slot for physical security, wireless LAN for fast network connectivity, and Bluetooth for stable wireless communication. The device’s sleek Stellar Gray finish complements professional environments, and its lightweight design makes it easy to deploy and relocate as needed. Included with the system are necessary accessories such as power cords, adapters, and installation inserts, ensuring a complete setup for immediate use. Whether for AI research, complex data analysis, or graphic-intensive applications, the Asus Ascent GX10-GG0010BN provides a robust, efficient platform tailored to high-demand workloads.
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