A DGX in the lab – a work-ready AI platform for the whole team
We configure DGX OS, CUDA and the NIM environment for the selected system, with resource sharing matched to the platform and the team’s workloads. The team starts working after setup and system checks.
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DGX OS, CUDA and NIM configured
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DGX Station: MIG resource allocation for the team
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A Station or 2× Spark as a compact fleet
What we deploy
What the development team gets
Resource allocation follows the selected platform and the team’s workloads.
01
A ready-to-work environment
DGX OS, CUDA tools and the NIM environment are installed and tested – the team does not spend weeks on platform setup.
02
MIG instances on DGX Station
On DGX Station, MIG partitions the supported GPU into isolated instances. We agree user and resource allocation according to workloads and available profiles.
03
Experiments on your data
Testing, fine-tuning and inference using data that must stay inside the company.
04
A base for every AI scenario
The same device serves the private assistant, RAG over documents and local inference – a platform, not a single solution.
On which hardware
Three variants – by team and model size
For a team we recommend the Station for MIG and memory. 2× Spark is a compact fleet when a quiet solution matters more than the maximum.
NVIDIA DGX Spark
A pilot and 1–2 users: a compact deskside device running from a regular outlet.
2× DGX Spark
A compact fleet for bigger jobs: two linked devices working together.
NVIDIA DGX Station
A team and larger models: MIG splits the GPU across users, up to 748 GB of coherent memory.
The configuration (model, memory, instance count) is worked out in the configuration consultation – before the order.
What is in the delivery
From order to a working system
- Delivery of an NVIDIA DGX Station (or 2× DGX Spark) to your location
- Installation of DGX OS, CUDA and the NIM environment – at your request
- For DGX Station: MIG instances configured to the agreed user and resource allocation
- Access, network and integration into the existing lab
- IT team training at your request. Warranty service through us – RMA with the manufacturer
After commissioning, the models and access are managed by your IT team – we stay with the equipment through warranty service.
Request an AI platform for the team
Tell us how many people use the platform and what they do – we will propose the system, resource allocation and commissioning plan.
FAQ
Frequently asked questions
NVIDIA
When this format is not the right choice
NVIDIA DGX Spark
A compact AI workstation for the desk: GB10 Grace Blackwell, 128 GB unified memory, ~240 W from a regular outlet.
NVIDIA DGX Station
A deskside AI supercomputer for large models and teams: GB300 Grace Blackwell Ultra, up to 748 GB of coherent memory.
NVIDIA RTX PRO Server
A rack GPU server: up to 8× RTX PRO 6000/4500 Server Edition, vGPU/MIG sharing for multiple users at once.