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What if NVIDIA's most powerful symbiosis of CPU and GPU design, originally intended for server infrastructure, is put inside a desktop form factor? That is exactly the question ASUS asked and decided to do. In its ExpertCenter Pro ET900N G3, ASUS utilized the NVIDIA GB300 Grace Blackwell Ultra desktop superchip, delivering 20 PetaFLOPS of FP4 precision compute, alongside 784 GB of unified cache-coherent memory, 288 GB of which are attributed to HBM3E on the GPU itself, and the remaining 496 GB to LPDDR5X embedded around the Grace CPU. With the GB300 "Blackwell Ultra," NVIDIA allows OEM partners to utilize its DGX blueprint and design their own desktops, so ASUS is undertaking a custom implementation here. To connect the monstrous compute to the outside world, ASUS embeds ConnectX-8 SuperNIC, which runs at 800 Gb/s, and installs NVIDIA's DGX OS, a Ubuntu fork with NVIDIA-specific kernel tweaks, optimizations, and additional drivers.
The motherboard powering the system is designed for serious expandability, offering three full-length PCIe x16 slots that allow you to stack multiple GPUs or specialized accelerators, including extra RTX cards for maximum compute performance, and three M.2 expansion slots for high-speed SSDs. Power delivery is interesting as it includes the standard ATX and EPS12V connectors for the CPU and system, plus three dedicated 12V‑2×6 GPU power connectors capable of supplying up to 1,800 Watts. Pricing and availability are unknown, but expect to pay $30,000+ for a system of this caliber, which is specifically aimed at AI researchers running these boxes as part of their larger infrastructure.
View at TechPowerUp Main Site | Source
The motherboard powering the system is designed for serious expandability, offering three full-length PCIe x16 slots that allow you to stack multiple GPUs or specialized accelerators, including extra RTX cards for maximum compute performance, and three M.2 expansion slots for high-speed SSDs. Power delivery is interesting as it includes the standard ATX and EPS12V connectors for the CPU and system, plus three dedicated 12V‑2×6 GPU power connectors capable of supplying up to 1,800 Watts. Pricing and availability are unknown, but expect to pay $30,000+ for a system of this caliber, which is specifically aimed at AI researchers running these boxes as part of their larger infrastructure.
View at TechPowerUp Main Site | Source
