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Blockchain NodesGPU Inference
$477.22/ month
- GPU
- 1x RTX PRO 4000 SFF
- VRAM
- 24 GB GDDR7 ECC
- CPU
- Core i5-13500, 14 cores
- RAM
- 64 GB DDR4
- Storage
- 2x 512 GB NVMe
GPU dedicated
Physical GPU servers for AI inference and training.
The whole machine is yours: CPU, RAM, NVMe and NVIDIA RTX PRO Blackwell GPUs with ECC memory. Run open-weight models on your own hardware and keep data inside the region you choose.
- From $477.22 / month
- Up to 4 GPUs
- GDDR7 ECC VRAM
- Root access
- GDDR7 ECC memory per GPU
- 24–96 GB
- Total VRAM on the four-GPU server
- 384 GB
- AI TOPS per RTX PRO 6000 Blackwell
- 3,511
- Network uplink on every server
- 1 Gbit/s
GPU Dedicated Servers
Pick your GPU server
Each server has a one-time setup fee of about one month's price.
GPU Training
$2,227.77/ month
- GPU
- 1x RTX PRO 6000 Max-Q
- VRAM
- 96 GB GDDR7 ECC
- CPU
- Core Ultra 7 265, 20 cores
- RAM
- 128 GB DDR5
- Storage
- 2x 1.92 TB NVMe
GPU Xeon
$3,342.77/ month
- GPU
- 1x RTX PRO 6000 Max-Q
- VRAM
- 96 GB GDDR7 ECC
- CPU
- Xeon Gold 5412U, 24 cores
- RAM
- 256 GB DDR5 ECC
- Storage
- 2x 960 GB NVMe
GPU Quad
$8,025.77/ month
- GPU
- 4x RTX PRO 6000 Max-Q
- VRAM
- 4x 96 GB GDDR7 ECC
- CPU
- Xeon Gold 5412U, 24 cores
- RAM
- 128 GB DDR5 ECC
- Storage
- 2x 1.92 TB NVMe
Prices in USD, billed monthly unless stated otherwise. Need a different size? Ask for a custom configuration.
What you get
Built in, not bolted on.
Single-tenant hardware
No hypervisor and no neighbors. Every CPU core, GPU and NVMe drive in the chassis works for you alone.
ECC GDDR7 memory
Every card uses GDDR7 VRAM with error correction, which guards long training runs against silent memory errors.
FP4 for faster inference
Blackwell GPUs support FP4 precision, which lowers memory needs and speeds up inference on quantized models.
Multi-instance GPU
RTX PRO 6000 servers support MIG, so one card can be split into isolated GPU instances for separate jobs or teams.
Your data, your region
Host open-weight models such as Mistral, Qwen or GLM on hardware you control in Istanbul or Nuremberg, without sending prompts to a third-party API.
Full root access
Install Ubuntu or Debian, your own driver and CUDA version, and manage the server the way you would on premises.
Use cases
What teams run on it.
-
01
Private LLM hosting
Run chat assistants and document analysis on open-weight models while internal files and customer data stay on your own server.
-
02
Training and fine-tuning
Train and adapt models on 96 GB cards, or spread larger jobs across four GPUs and 384 GB of VRAM in one machine.
-
03
High-volume inference
Serve many concurrent requests for chatbots and internal assistants, or run several models side by side on the four-GPU server.
-
04
Generative media and analytics
Generate images with diffusion models, transcribe and translate speech, or speed up large statistical and machine learning jobs.
Specifications
The details.
| Spec | Value |
|---|---|
| RTX PRO 4000 Blackwell SFF | 24 GB GDDR7 ECC, 770 AI TOPS, 280 Tensor Cores |
| RTX PRO 6000 Blackwell Max-Q | 96 GB GDDR7 ECC, 3,511 AI TOPS |
| Precision | FP4 support on all Blackwell GPUs |
| Multi-instance GPU | RTX PRO 6000 servers |
| Xeon server RAM | 256 GB, configurable up to 768 GB DDR5 ECC |
| Network | 1 Gbit/s uplink |
| Operating systems | Ubuntu, Debian (64-bit) |
How it works
Three steps to running.
-
01
Choose a server
Pick the GPU, memory and storage mix that fits your models.
-
02
We prepare it
Select Istanbul or Nuremberg and an operating system. We set up the server and send your access details.
-
03
Deploy your models
Log in as root over SSH, install your CUDA stack and start serving or training.
Questions
GPU Dedicated Servers, answered.
Are these servers suitable for AI training?
It depends on model size. The 96 GB RTX PRO 6000 servers handle training for most popular models; for very large models, the four-GPU server gives you 384 GB of VRAM in total.
Do the GPUs support CUDA?
Yes. Every server uses NVIDIA RTX PRO Blackwell GPUs with full CUDA support.
Can I add GPUs to a single-GPU server?
No. The single-GPU models are fixed at one card. If you need more, choose the four-GPU server.
Is there a setup fee?
Yes. Each server has a one-time setup fee of about one month's price, on top of the monthly rate.
Can I get more RAM?
The Xeon single-GPU server starts with 256 GB of DDR5 ECC memory and can be configured with up to 768 GB.
Should I choose GPU cloud or a dedicated GPU server?
A GPU cloud server is a virtual machine with one passed-through card. A dedicated server gives you the whole physical machine, more RAM and storage, and up to four GPUs.
More cloud
Often used together.
Cloud VPS
Virtual machines in four families, from shared-CPU starters to dedicated, memory-heavy servers.
From $12 / month
ExploreGPU Cloud Servers
Virtual servers with a full NVIDIA RTX PRO 6000 and 96 GB of VRAM for AI, rendering and simulation.
From $2,398 / month
ExploreManaged Kubernetes
Kubernetes clusters with a managed control plane. You pick the worker nodes; we run the rest.
From $24 / month
ExploreTalk to an engineer
Not sure which size fits?
Tell us what you want to run. An engineer suggests a configuration and sends you a written price.