GIGABYTE Launches AI TOP ATOM Supercomputer Powered by NVIDIA GB10
Taipei-based GIGABYTE has announced in a press release that its new AI TOP ATOM personal supercomputer will be available globally starting October 15. The system is built around the NVIDIA Grace Blackwell GB10 Superchip and is designed for local AI development, offering supercomputer-level performance in a desktop form factor.
The AI TOP ATOM comes with 128 GB of unified system memory, up to 4 TB of SSD storage, and delivers FP4 AI performance of up to 1 PetaFLOP. It supports large-scale models with up to 200 billion parameters and can be paired with another unit via an integrated NVIDIA ConnectX-7 network card to handle models with up to 405 billion parameters.
Preloaded with the NVIDIA AI Software Stack, the system enables rapid prototyping, fine-tuning, and inference. It also integrates with GIGABYTE’s AI TOP Utility software, providing a user interface for managing large language models, multimodal models, and machine learning applications. The AI TOP ATOM is aimed at developers, researchers, and institutions seeking a compact, energy-efficient AI computing solution.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like Silicon Brief or Daily AI Brief.
Also, consider following us on social media:
More from Data Centers
Sep 29 Efficient Computer Raises $97 Million to Scale Its Processors Sep 29 Hikvision Adds HIKO AI Engine to Hik-Connect 7 Sep 29 Compal to Show NVIDIA AI Factory Infrastructure at OCP Summit Sep 29 Samsung Invests $1 Billion in KKR's Helix Data Center Platform Sep 29 Cerebras to Supply 100 Megawatts of AI Systems to Gimlet LabsSilicon Brief
Weekly coverage of AI hardware developments including chips, GPUs, cloud platforms, and data center technology.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
Acer Shows Compact RTX Spark Desktop Design at IFA 2026
Acer Introduces Veriton RI110 AI Mini Workstation
Aitech Introduces Rugged AI Boards for Defense Systems
ASUS Expands AI Infrastructure From Cloud to Edge
AEWIN Introduces Liquid Cooled AMD EPYC 9006 AI Servers
Daily AI Brief: the AI news that matters, in your inbox.