NVIDIA DGX Station: Deskside AI Supercomputer Features

Building, fine-tuning, and deploying large AI models shouldn’t require waiting in a cloud queue—or compromising on performance because your workstation can’t keep up. NVIDIA DGX Station introduces a new class of deskside AI supercomputer designed from the ground up for modern AI development. Powered by the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip and backed by a massive 775GB+ of large coherent memory, DGX Station brings data-center-grade AI capabilities directly to your desk for training and inference at scale.

What Is NVIDIA DGX Station?

NVIDIA® DGX Station™ is the ultimate deskside AI development platform, purpose-built for teams and individuals running demanding AI workloads locally. It combines the latest NVIDIA hardware innovations with the NVIDIA AI Software Stack to create an integrated system that accelerates everything from data science experimentation to large-scale model training and inferencing.

Unlike traditional workstations that stitch together separate CPUs and GPUs with limited memory coherence, DGX Station is built around a tightly coupled CPU+GPU architecture connected through NVIDIA NVLink®-C2C. The result is exceptionally fast system communication and a large, unified memory pool that reduces bottlenecks when models and datasets grow.

Why DGX Station Matters for Modern AI Workloads

AI models continue to scale rapidly, and developers often hit constraints in three places: compute throughput, memory capacity, and data movement. NVIDIA DGX Station addresses all three with a balanced architecture designed specifically for AI:

High AI compute for training and inference on advanced models
Large coherent memory that enables bigger models and larger batch sizes without excessive swapping
High-bandwidth interconnect between CPU and GPU to keep pipelines fed and reduce idle time

Key Technological Breakthroughs in NVIDIA DGX Station

NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip

At the center of DGX Station is the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip. This architecture pairs an NVIDIA Blackwell Ultra GPU with a high-performance NVIDIA Grace CPU, connected via NVLink-C2C for best-in-class CPU–GPU communication. This design is built to handle end-to-end AI workflows where CPU preprocessing and GPU acceleration must operate as a unified system.

Fifth-Generation Tensor Cores with FP4 Support

NVIDIA DGX Station leverages fifth-generation Tensor Cores in the Blackwell generation GPU, enabling 4-bit floating point (FP4) AI. FP4 can increase throughput and expand the size of next-generation models that can fit in memory while maintaining high accuracy—an advantage for teams pushing model scale and iteration speed.

NVIDIA NVLink-C2C Interconnect: Coherent CPU–GPU Data Movement

NVIDIA NVLink-C2C extends NVLink technology to a chip-to-chip interconnect between GPU and CPU. This provides high-bandwidth coherent data transfers across processors and accelerators, helping eliminate the bottlenecks common in traditional CPU+GPU systems where data frequently hops across slower links.

Large Coherent Memory for AI: One Big Pool for Massive Models

One of the defining advantages of DGX Station is its massive coherent memory pool—designed to keep large models and workloads running efficiently in one unified memory space. As models grow in parameters and context windows, having a large, coherent memory architecture can reduce fragmentation, improve throughput, and simplify development workflows that otherwise require complex sharding strategies.

NVIDIA ConnectX-8 SuperNIC: Up to 800Gb/s for Scaling and Data Access

Local compute is only part of the story—AI teams also need fast connectivity to data stores, shared infrastructure, and multi-node resources. DGX Station integrates the NVIDIA ConnectX®-8 SuperNIC™, delivering up to 800Gb/s of peak bandwidth via Ethernet. This is designed to accelerate hyperscale AI computing patterns and improve performance when DGX Station is used as part of a larger “AI factory” environment.

DGX Base OS + NVIDIA AI Software Stack: From Desktop to Data Center

DGX Station runs on NVIDIA DGX OS, a stable, fully qualified operating system environment tailored for AI, machine learning, and analytics on DGX platforms. It includes system-specific configurations, drivers, and diagnostic/monitoring tools. Just as importantly, the broader NVIDIA CUDA-X AI Platform and NVIDIA AI software stack provide a full-stack solution for:

Model fine-tuning
Inference deployment
Data science workflows
Using consistent tools, libraries, frameworks, and pretrained models from desktop to cloud

This consistency makes it easier to prototype locally and then scale to NVIDIA DGX Cloud or other accelerated infrastructure without rewriting your workflow.

Workload-Optimized Power-Shifting

DGX Station includes AI-based system optimizations that intelligently shift power based on the active workload. This helps continually maximize performance and efficiency, adapting to whether you’re doing heavy training, inference serving, or mixed workloads.

NVIDIA DGX Station Specifications (Highlights)

Below are key specs that illustrate why DGX Station is positioned as a true deskside AI supercomputer:

GPU: 1× NVIDIA Blackwell Ultra
CPU: 1× Grace (72-core Neoverse V2)
GPU Memory: 279GB HBM3e (8 TB/s)
CPU Memory: 496GB LPDDR5X (396 GB/s)
NVLink-C2C: 900 GB/s
Networking: NVIDIA ConnectX-8 SuperNIC, up to 800 Gb/s Ethernet
MIG: Up to 7 instances
Storage: 4× M.2 Gen 5 slots
Power: 1600W total system power

In addition, DGX Station is described as delivering up to 20 petaflops of AI performance and up to 784GB of unified system memory (depending on configuration references), supporting extremely large models and demanding AI pipelines.

How DGX Station Supports Teams: Personal Supercomputer or Shared AI Node

DGX Station can be deployed in two high-value ways:

Single-user powerhouse: A dedicated deskside system for a researcher or engineer running advanced models on local data with minimal latency and maximum control.
Centralized on-demand compute: A shared node for a team, acting like a “personal cloud” for internal data science and AI development.

Multi-Instance GPU (MIG): Up to Seven Isolated Environments

With NVIDIA Multi-Instance GPU (MIG), DGX Station can partition into as many as seven instances, each with its own high-bandwidth memory, cache, and compute cores. This is ideal for teams that want concurrent workloads—such as multiple experiments, parallel fine-tunes, or separate inference services—without resource contention.

Practical Benefits for AI Development and Deployment

When you combine coherent memory, fast CPU–GPU interconnect, modern Tensor Cores, and high-speed networking, DGX Station enables tangible workflow improvements:

Faster iteration loops: Train, test, and refine models locally without waiting for remote resources.
Support for larger models: Unified memory design helps accommodate bigger parameter counts and datasets.
Smoother scaling: Use the same NVIDIA software stack from desktop to DGX Cloud or data center.
Team enablement: MIG makes it easier to share one system across multiple users and projects.

FAQ: NVIDIA DGX Station

Is NVIDIA DGX Station meant for training or inference?

Both. DGX Station is built for large-scale AI training and inferencing workloads, offering high compute performance and a large coherent memory pool for demanding models.

What makes DGX Station different from a typical AI workstation?

DGX Station is designed as an integrated AI system with Grace + Blackwell Ultra, NVLink-C2C coherent interconnect, massive coherent memory, and a qualified software environment (DGX OS)—not just a commodity CPU paired with a GPU.

Can DGX Station be shared by a team?

Yes. It can act as a centralized compute node, and MIG enables partitioning into up to seven isolated GPU instances to support multiple concurrent users or workloads.

How does 800Gb/s networking help?

With NVIDIA ConnectX-8 SuperNIC supporting up to 800Gb/s, DGX Station can move data quickly to and from storage, connect efficiently to other systems, and support multi-station scaling—critical for data-intensive AI workflows.

Conclusion: A New Standard for Deskside AI Computing

NVIDIA DGX Station brings the performance profile of modern AI infrastructure to a deskside form factor—pairing the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip, large coherent memory, NVLink-C2C, and ConnectX-8 SuperNIC into a unified platform. For teams and individuals building next-generation AI, DGX Station offers a compelling path to faster experimentation, larger models, and smoother scaling from desktop to cloud and data center using the same NVIDIA software stack.