NVIDIA GPUs, ready for production AI.
Complete GPU isolation per VM. Pre-configured CUDA stack, NVLink for multi-GPU training, MIG support on A100. Your models train on dedicated hardware — no shared resources, no performance variance.
From inference to large-scale training.
4 GPU models across 3 NVIDIA architectures. Pick the right card for your workload.
Pre-installed. Start training now.
Every instance ships with the full NVIDIA + ML stack. No setup, no driver hell.
GPU instances for every scale.
GPU clusters across Europe.
3 datacenters with dedicated GPU racks. Each region offers full NVIDIA GPU isolation, NVLink-capable nodes and GDPR-compliant data residency. Your training data stays in the EU.
Every GPU metric, live.
GPU utilization, VRAM, temperature, TFLOPS throughput and power draw — all streamed to your console. Compatible with Prometheus, Grafana and custom exporters.
GPU infra as code.
Provision GPU clusters with Terraform, automate training pipelines with our SDKs. Your ML infrastructure lives in Git.
Your training data stays in Europe.
Built for compute-intensive workloads.
From model training to real-time inference — our GPU instances deliver dedicated NVIDIA hardware with predictable performance and transparent pricing.
LLM fine-tuning & training
Multi-GPU A100 setups with NVLink for fast gradient sync. Train LLaMA, Mistral or custom models with full HBM2e memory.
Model serving at scale
Tesla T4 instances optimized for inference with TensorRT. Low latency, high throughput, cost-efficient for production APIs.
Blender, V-Ray, Unreal Engine
RTX 4090 with Ada Lovelace ray tracing cores. Scale rendering farms on demand, pay by the hour.
Molecular simulation & HPC
A100 clusters with NVSwitch and InfiniBand for massively parallel workloads. CUDA-optimized for maximum throughput.
Yes. Each GPU is exclusively allocated to your VM. No sharing, no time-slicing. A100 GPUs also support MIG (Multi-Instance GPU) if you want to partition a single GPU into isolated sub-instances.
All instances ship with CUDA 12.x, cuDNN 8.9, TensorRT 8.6, PyTorch 2.x, TensorFlow 2.x, and Jupyter Lab with ML extensions. Custom images with your own stack are also supported.
GPU-QUANTUM (2× A100) and above include NVLink for fast GPU-to-GPU communication at 600 GB/s bidirectional. GPU-CLUSTER and GPU-SUPERCOMPUTE use NVSwitch for full-bandwidth any-to-any GPU communication.
GPU instances are provisioned in under 2 minutes. This includes GPU allocation, driver initialization, CUDA stack verification and NVMe warming. The image is pre-cached for instant availability.
Yes. All GPU plans support hourly billing. A refundable deposit is required: 50% of the equivalent monthly price (all GPU plans exceed the €50 threshold). You only pay actual usage at end of month.