GPU power for AI workloads, on demand.
Train, fine-tune and serve models on NVIDIA-accelerated nodes — high VRAM, NVMe scratch and 10 Gbit networking, with CUDA and drivers ready to go. Hourly or monthly, and yours to run however you like.
Built to train and serve.
NVIDIA acceleration, the VRAM to fit real models, and a CUDA baseline that's ready the moment you connect.
NVIDIA GPUs
Accelerated compute for training, fine-tuning, inference and rendering.
High VRAM
From 24 GB up — fit larger models, longer context and bigger batches.
CUDA & drivers ready
NVIDIA drivers and CUDA pre-installed — start training without the setup pain.
NVMe scratch storage
Fast local scratch for datasets, checkpoints and intermediate artifacts.
Hourly or monthly
Spin up by the hour for experiments, or reserve monthly for 24/7 jobs.
Windows or Linux
Run your ML stack on the OS you prefer — Linux recommended for most workflows.
The GPU box is yours to drive.
You get root access and a ready CUDA environment, while we keep the GPU hardware, drivers and network running underneath you.
Ready for you The GPU platform
Hardware & drivers, ready to use.
- GPU provisioning & OS install
- NVIDIA drivers + CUDA baseline
- Network, host & data centre
- Failed-component replacement
- DDoS protection
Yours to control Your ML stack
Your code, your models, your runs.
- Full root or Administrator access
- Your models, data & code
- Frameworks & library versions
- Training jobs, tuning & schedules
- Users, keys & security
Drivers and CUDA are pre-installed, so you can start training the moment you connect. Spin up by the hour or reserve monthly — with full root access and NVMe scratch on every node.
From experiments to production.
Model training
Train and fine-tune LLMs and vision models on dedicated GPUs.
Inference & serving
Deploy models behind APIs with low-latency GPU inference.
Fine-tuning
LoRA/QLoRA and full fine-tunes on budgets that fit your VRAM.
Rendering & 3D
GPU rendering for Blender, video and CGI pipelines.
Batch & data
GPU-accelerated ETL, embeddings and large batch processing.
Research & notebooks
Spin up Jupyter and prototype, then tear down when you're done.
Pay by the hour, or reserve monthly.
Run experiments hourly and only pay for what you use, or reserve a node monthly for 24/7 jobs at a lower effective rate.
Single GPU
- 24 GB VRAM
- 8 vCPU · 32 GB RAM
- NVMe scratch
- CUDA + drivers ready
Dual GPU
- 48 GB VRAM
- 16 vCPU · 64 GB RAM
- NVMe scratch
- Multi-GPU ready
Quad GPU
- 96 GB VRAM
- 32 vCPU · 128 GB RAM
- NVMe scratch
- 10 Gbit network
All GPU nodes: NVIDIA-accelerated · CUDA + drivers pre-installed · NVMe scratch · 10 Gbit network · Windows or Linux.
The stack, in plain terms.
GPU & compute
Control
Network & storage
Pre-installed & available
A ready CUDA baseline — bring your own framework versions or use our images.
*Fair-use applies. Framework versions are your choice; we provide the driver/CUDA baseline.
The honest answers.
NVIDIA-accelerated nodes with high VRAM (24 GB and up, depending on plan). The exact model is shown at order time; if you have a specific requirement, ask us before you deploy.
Both. Spin a node up by the hour for experiments and pay only for what you use, or reserve it monthly for 24/7 jobs at a lower effective rate.
Yes — NVIDIA drivers and a CUDA baseline are ready when you connect. You install your own framework versions (PyTorch, TensorFlow, etc.) or use our images.
Yes. The NVIDIA Container Toolkit is available so you can run GPU-accelerated containers and reproducible environments.
Yes — full root or Administrator access to the node. Install any framework or library version, run containers, configure your environment however you like, and reinstall any time from the client area.
Both are supported. Linux is recommended for most ML workflows, but Windows is available if your tooling needs it.
In EU data centers with transparent data handling. Availability can vary by region, so check at order time.