ASUS ExpertCenter Pro ET900N G3-8T-03 8TB RTX PRO 4000 Blackwell

Key features of the ASUS ExpertCenter Pro ET900N G3-8T-03 8TB RTX PRO 4000 Blackwell workstation

GB300 Grace Blackwell Ultra superchip and 748GB of coherent memory

At the heart of the ASUS ExpertCenter Pro ET900N G3-8T-03 is the NVIDIA GB300 Grace Blackwell Ultra superchip – a unique combination of a Blackwell Ultra GPU with 252GB of HBM3e memory and 7.1TB/s throughput, and a 72-core Grace Neoverse V2 Arm processor with 496GB of LPDDR5X memory and 396GB/s throughput. Both parts of the superchip are connected by NVLink-C2C technology with 900GB/s bandwidth, which eliminates the traditional bottleneck between CPU and GPU memory and creates a single 748GB coherent memory space. The result is 20 petaFLOPS of FP4 performance and 330 TOPS of Int8 performance – right where an entire server room floor was once needed.

NVIDIA MIG and ConnectX-8 for team AI performance in a single device

The ASUS ExpertCenter Pro ET900N G3-8T-03 is not just a personal supercomputer, but a powerful local compute node for an entire AI team. NVIDIA MIG technology partitions the GPU into up to seven fully isolated instances, each with its own dedicated memory, compute cores, and network connection, allowing multiple researchers to work concurrently without interfering with each other. Connecting two stations via the NVIDIA ConnectX-8 SuperNIC with a bandwidth of up to 800Gb/s then doubles the performance to over 40 petaFLOPS and over 1.4TB of coherent memory for working with the largest available models – without the need to build an expensive server cluster.

Limitless AI application prototyping and development

The complete NVIDIA AI software stack, combined with NVIDIA OpenShell, provides developers with a full-featured platform for creating AI models, AI agents, and AI-enhanced applications – all locally, without cloud dependency. NVIDIA DGX OS comes preconfigured with CUDA-X libraries, Container Toolkit, Docker, and NVIDIA AI Enterprise for an immediate start to development. Once the solution is ready for production deployment, the ASUS ExpertCenter Pro ET900N G3-8T-03 enables direct and seamless migration of workloads to the NVIDIA DGX Cloud or other NVIDIA-accelerated infrastructure – using the same tools, libraries, and pre-trained models.

Fine-tune AI models with billions of parameters right at your desk

Use the ASUS ExpertCenter Pro ET900N G3-8T-03 to fine-tune pre-trained models with billions of parameters directly at your workstation. Training on your own data allows you to specialise AI models for specific industry data or use cases – without sending sensitive data to the cloud or paying for expensive GPU instances. The NemoClaw platform then ensures the secure and isolated operation of autonomous AI agents with full governance control. The result is a model precisely tailored to your scenario, created locally, securely, and under your complete control.

ASUS ExpertCenter Pro delivers data science at the highest level

The combination of 748GB of coherent memory and 20 petaFLOPS of performance makes the ASUS ExpertCenter Pro ET900N G3-8T-03 the ideal workstation for demanding data analytics and machine learning projects. Extensive data sets, complex computational models, and demanding training processes that previously required a powerful cloud cluster or dedicated server now run right on your desk. Four M.2 NVMe slots – two PCIe 5.0 x2 and two PCIe 6.0 x4 for training data – two 400Gb/s QSFP network interfaces, and three PCIe 5.0 x16 slots ensure data throughput worthy of a research facility – fast, local, and without waiting for remote infrastructure.

Inference for models with up to 1 trillion parameters

The Blackwell Ultra architecture's Tensor Cores, with support for the FP4 format, achieve performance of up to 20 petaFLOPS. Combined with 748GB of coherent memory, they enable you to run inference on the largest available AI models, with up to 1 trillion parameters, directly on your desktop. Test, validate, and deploy models locally and in real time – without cloud latency, shared infrastructure, or the risk of sensitive data leaks. NVIDIA NIM microservices then allow for the rapid deployment of optimised models as containers for production inference.