What Workloads Get the Most from NVIDIA DGX Station

A Data Center on Your Desk

The NVIDIA DGX Station™ is not a workstation in the traditional sense. It is part of a new class of computers, designed from the ground up to build and run AI. DGX Station brings data center-level performance to your desk, designed to run the kinds of AI workloads that normally require a rack of servers and a cloud contract.

It is an expensive, narrow-purpose tool. But for the right user, it solves a real problem: how do you run serious AI workloads privately, at scale, without paying cloud costs indefinitely?

In a nutshell:

• Private, on-prem AI at data center scale: DGX Station GB300 brings server-class LLM inference and fine-tuning to a deskside system.
• The big unlock is 748GB unified memory: Enables much larger models and longer contexts than typical workstations.
• Better model quality with fewer compromises: More headroom to stay closer to BF16/FP16 instead of aggressive quantization.
• Built for enterprise realities: Supports data sovereignty, compliance, and predictable costs versus metered cloud GPU spend. It also lets you develop straight on DGX OS that can be immediately deployed into your DGX and HGX systems.
• Strong for more than LLMs: Also fits vision, time-series, recommender systems, GNNs, and multi-modal workloads.
• Ideal users: Teams with sensitive data, heavy sustained GPU usage, or agentic systems that need always-on local inference.

NVIDIA DGX Station Specifications

The DGX Station is built around NVIDIA's GB300 Grace™ Blackwell Ultra Desktop Superchip (Exxact VWS-158270643), which fuses a 72-core ARM-based Grace CPU and a Blackwell Ultra GPU onto a single chip connected by a 900 GB/s NVIDIA NVLink®-C2C interface. That interconnect is what makes the unified memory architecture work: both the CPU and GPU share the same memory pool rather than treating each side as separate components that pass data back and forth over a slower PCIe bus.

The numbers that matter most for AI work:

• 748GB of unified memory: 252GB HBM3e on the GPU side running at 7.1 TB/s of bandwidth, plus 496GB LPDDR5X on the CPU side at 396 GB/s. Both pools are coherent, meaning either processor can address the full 748GB without explicit data transfers between them.
• Up to 20 petaFLOPS of AI compute in FP4 precision, the format used for large-scale inference workloads
• MIG (Multi-Instance GPU) support, which partitions the physical GPU into up to 7 fully isolated instances, each with its own dedicated HBM slice, cache, and compute cores
• ConnectX®-8 SuperNIC™ with dual 400GbE QSFP112 ports, allowing two DGX Stations to be directly linked over high-speed fabric for multi-node workloads
• Three PCIe Gen 5 x16 slots for adding discrete GPUs, storage, or other accelerators

One important caveat: the base system has no video output. The GB300 Superchip is a compute-only part. If you want to connect a monitor, you will need to add a GPU with discrete graphics when configuring this system. For most teams, this will be a headless compute node accessed over the network anyway. Supported GPUs include: NVIDIA RTX PRO™ 2000 Blackwell, RTX PRO 4000 Blackwell SFF, RTX PRO 6000 Workstation Edition, RTX PRO 6000 Blackwell Max-Q.

What Workloads the NVIDIA DGX Station Excels At
Running Large Language Models Locally

The most straightforward use case is also the most compelling one for enterprises: running large language models without data leaving your building.

Most high-end consumer workstations, even with an RTX 5090, top out at 32GB of VRAM. A quad-GPU workstation with 4x NVIDIA RTX PRO 6000 Blackwell GPUs reaches 384GB of VRAM. That limits you to models in the 7B to 70B parameter range before you start making serious quality trade-offs through quantization.

The DGX Station's 748GB of unified memory changes that ceiling entirely. A 405B parameter model like Llama 3.1 405B in full BF16 precision requires roughly 810GB of memory. With the unified pool and some optimization, you can run it.

For industries where data privacy is non-negotiable—in healthcare, legal, defense, and finance, for example—the ability to run a frontier-scale model entirely on your own hardware is a requirement, not a luxury.

• Data sovereignty: keep prompts, context, and outputs inside the organization (helps with compliance and IP protection).
• Model quality vs. quantization: more memory means you can stay closer to BF16/FP16 instead of forcing 4-bit/8-bit quantization.

Open Weights Model Deployment

The open weights ecosystem has matured to the point where models like Meta's Llama 3, Mistral, DeepSeek R1, Qwen, and Google's Gemma are genuinely competitive with many closed API models on standard benchmarks. The difference is that you can download them, modify them, and serve them without API fees, rate limits, or data leaving your infrastructure.

On a consumer GPU, you are typically running quantized versions of these models, often at 4-bit or 8-bit precision, to fit within VRAM constraints. Quantization reduces model quality, sometimes noticeably on complex reasoning tasks. On the DGX Station, you can serve full-precision BF16 versions, which is what the published benchmark numbers are actually measured against. Using a serving layer like vLLM, you can expose these models as an OpenAI-compatible API endpoint on your local network, meaning existing tools and integrations work without modification.

Supplemental takeaways:

• Security + compliance: keep weights, logs, prompts, and customer data on-prem; tighten controls with network segmentation and audit trails.
• Cost predictability: shift from per-token pricing to fixed-capex + power/ops; better for sustained, high-volume inference.
• Customization: fine-tune or apply domain adapters and deploy them alongside base models

Fine-Tuning & Custom Model Training

If you're doing more than inference—adapting models to your domain—DGX Station is essentially a local training node with enough memory headroom to fine-tune large open models without the usual workstation compromises.

Common fine-tuning workflows it enables:

• LoRA/QLoRA for fast, cost-effective adaptation of LLMs to internal terminology, ticket histories, product docs, and support chats
• Full-parameter fine-tuning for smaller/medium models when maximum accuracy is required (and when data governance demands everything stays on-prem)
• Embedding model training and reranker tuning to improve retrieval quality for RAG systems (often a bigger win than the base LLM)

Why the platform matters:

• Memory capacity = fewer compromises: larger batch sizes, longer context windows, and less aggressive quantization during training
• Faster iteration loops: engineers can run experiments continuously without waiting for cloud capacity or burning GPU-hour budget
• Privacy + compliance: sensitive datasets (customer logs, medical/legal docs, internal IP) can remain fully local

Data Science & Deep Learning (Beyond LLMs)

While DGX Station is easy to frame as an "LLM box," it also maps cleanly to classic deep learning and data science workloads where teams need large, fast memory and predictable throughput.

Strong-fit examples:

• Computer vision: training and evaluation for detection/segmentation pipelines (manufacturing QA, medical imaging, remote sensing)
• Time-series + forecasting: deep learning for anomaly detection, predictive maintenance, and finance signals
• Recommendation and ranking models: large embedding tables and feature sets that benefit from memory headroom
• Graph neural networks (GNNs): link prediction and graph classification on large, dense graphs
• Multi-modal models: vision-language systems that combine large encoders and require more memory than typical workstations

For many teams, the practical benefit is simple: a single deskside system can handle both the ETL/feature work (CPU) and the training/inference work (GPU) without pushing everything into a shared cloud environment.

OpenClaw and Always-On AI Agents

OpenClaw is an open-source AI agent framework built around the idea of a persistent, local-first AI assistant. Unlike a chat interface you open and close, OpenClaw runs continuously in the background, maintains context from your files, emails, and applications, and can autonomously execute tasks like drafting responses, scheduling, and multi-step research combining local documents with web search.

NVIDIA built NeMo™Claw as a dedicated stack for running OpenClaw on their hardware. It installs via a single command and layers three things on top of the base OpenClaw setup:

• Local model inference via NVIDIA's Nemotron open models, including Nemotron 3 Super at 120B parameters (with 12B active parameters via a mixture-of-experts architecture), served through Ollama or vLLM
• OpenShell, a sandboxed container runtime that enforces policy-based security guardrails, so the agent operates within defined boundaries around filesystem access, network access, and data handling
• A privacy-aware inference router that can direct sensitive tasks to local models while optionally routing heavier reasoning tasks to cloud endpoints when needed

The DGX Station matters here because OpenClaw's quality scales directly with the size of the model running underneath it. On a consumer GPU you are realistically running a 7B or 13B model as the agent brain, which limits reasoning depth.

On the DGX Station you can run Nemotron 3 Super 120B continuously, with MIG instances available for other parallel workloads alongside it. On NVIDIA's PinchBench, a benchmark designed to measure LLM performance specifically in the OpenClaw agentic context, Nemotron 3 Super scores 85.6%, currently the top result among open models.

Who Is Using the NVIDIA DGX Station?

This machine makes sense for:

• AI research teams working with proprietary or sensitive data that cannot leave the organization, including clinical researchers, legal AI teams, defense contractors, and financial institutions
• Organizations doing cloud repatriation that are already spending significant monthly sums on GPU cloud instances for inference or fine-tuning; the upfront cost looks different when spread over an 18 to 24 month horizon against ongoing cloud spend
• Teams building agentic systems or Physical AI pipelines who need a local development environment that mirrors what they will eventually deploy at data center scale; the identical software stack means no re-engineering when moving to production
• Small AI labs and research institutions that need server-grade compute without the infrastructure overhead of a full rack deployment

Other alternatives to the NVIDIA DGX Station are multi-GPU workstations featuring NVIDIA RTX PRO GPUs. This includes:

• Individual developers whose workloads that can fit on a single GPU.
• Hobbyists or enthusiasts exploring AI casually. The NVIDIA DGX Spark™ is an amazing system for this.
• Anyone needing strong general-purpose CPU performance; the Grace CPU's 72 ARM Neoverse V2 cores do not match the needs for rendering and simulation workloads that necessitate modern Intel Xeon or AMD EPYC processors for traditional CPU-bound tasks.

How DGX Station Stacks Up Against Cloud

The cost for an NVIDIA DGX Station is an investment and worth framing it against the alternative. Cloud based computing and/or API usage.

On-demand H100 GPU instances on AWS (p5.xlarge) run around $10 to $12 per GPU-hour. A team running continuous inference on a large model, or running regular fine-tuning jobs at scale, can reach $8000 - $10,000 on modest usage or $15,000 to $20,000 on high usage per month in cloud GPU spend. At that rate, the DGX Station has drastically lower TCO, paying for itself within the year.

Beyond the math, there is the predictability argument. Cloud GPU availability is not guaranteed, pricing changes, and usage can spike at inopportune times. Owning the hardware means fixed, known compute costs and guaranteed availability.

Conclusion

The DGX Station shifts how AI infrastructure is being thought about. For years, the default assumption was that serious AI computing lives in the cloud or in large on-prem data centers. At Exxact, we have always believed in on-prem computing with our configurable workstations and servers.

What NVIDIA DGX Station offers is to bring AI to the user by giving them both the compute and the access directly: the NVIDIA DGX Spark for individuals and small teams, the DGX Station for teams and labs, and full rack DGX and HGX systems for enterprise scale. The software stack is consistent across all of them.

As an Elite NVIDIA Solutions Partner, Exxact can deliver on all fronts, from proof of concept to full-scale deployment. Talk to an engineer today for more information on the DGX Station as well as DGX Spark and DGX/HGX Blackwell.