GPU Server Switzerland
Dedicated GPU power for AI, machine learning, 3D rendering and scientific computing. NVIDIA GPUs with AMD Threadripper from Swiss data centres.
GPU Computing Power
Up to 125 TFLOPS FP32 computing power and 96 GB vRAM with dedicated NVIDIA graphics cards. Thousands of CUDA cores massively accelerate AI training, inference and parallel computations.
GDPR & Swiss Location
Your data and AI models stay in Switzerland. Our GPU servers are located in data centres in Zug/Zurich and are subject to Swiss data protection law – ideal for sensitive data and compliance.
Versatile Applications
From AI training to 3D rendering to scientific simulations – our GPU servers are optimised for every GPU-intensive task. Linux or Windows, full root access, PCIe 4.0 connectivity.
A GPU server from FireStorm combines the computing power of modern NVIDIA graphics cards with the reliability of dedicated server hardware. GPU servers are specifically designed for tasks requiring massive parallel computations: artificial intelligence, machine learning, 3D rendering, scientific simulations and much more. Thanks to thousands of CUDA cores, GPUs process these workloads up to a hundred times faster than conventional CPUs.
GPU Starter
CHF 599.–/mo
Setup: CHF 290.–
- AMD Threadripper 3970X (32C/64T)
- 256 GB RAM DDR4 ECC
- NVIDIA RTX 4000 SFF Ada
- 20 GB GDDR6 ECC vRAM
- 19.2 TFLOPS FP32 / 6,144 CUDA Cores
- 8 TB NVMe RAID 10
- 1 Gbit/s Uplink
- 1x IPv4 + IPv6
GPU Pro
CHF 749.–/mo
Setup: CHF 1,900.–
- AMD Threadripper 3970X (32C/64T)
- 256 GB RAM DDR4 ECC
- NVIDIA RTX PRO 6000 Blackwell Max-Q
- 96 GB GDDR7 vRAM
- 125 TFLOPS FP32 / 24,064 CUDA Cores
- 8 TB NVMe RAID 10
- 1 Gbit/s Uplink
- 1x IPv4 + IPv6
What are TFLOPS and why do they matter?
TFLOPS (Tera Floating Point Operations Per Second) measure a GPU's computing power in trillions of floating-point operations per second. The higher the TFLOPS value, the faster the GPU can perform mathematical calculations – this is crucial for AI training, where billions of parameters need to be optimised. The RTX 4000 SFF Ada delivers 19.2 TFLOPS, while the RTX PRO 6000 Blackwell computes more than 6x faster at 125 TFLOPS.
vRAM – the key to large AI models
A GPU's Video RAM (vRAM) determines how large models and datasets can be processed simultaneously. Fine-tuning large language models (LLMs) or high-resolution 3D rendering requires a lot of vRAM. With 20 GB vRAM, the RTX 4000 SFF Ada is suitable for medium-sized models and standard rendering, while the RTX PRO 6000 Blackwell with 96 GB vRAM handles even the largest AI models and most complex simulations.
Use cases
- AI & ML Training: Train neural networks, computer vision models or NLP systems directly on your dedicated GPU server.
- LLM Fine-Tuning: Adapt large language models like LLaMA, Mistral or Falcon to your specific requirements.
- Inference: Run AI models in production with low latency and high throughput.
- 3D Rendering & Visualisation: Blender, V-Ray, OctaneRender – GPU-accelerated rendering for architecture, film and design.
- Video Encoding: Hardware-accelerated encoding with NVENC for streaming platforms and media production.
- Scientific Simulations: CFD, molecular dynamics, climate modelling – CUDA-accelerated computations for research.
- Data Analysis: GPU-accelerated data processing with RAPIDS, cuDF and cuML for big data applications.
- CAD/CAE: GPU-supported computations for engineering and construction.
Swiss location – GDPR-compliant
All GPU servers are located in our data centres in Zug/Zurich, Switzerland. Your data, AI models and training data never leave Switzerland and are subject to the strict Swiss Data Protection Act. This is particularly relevant for companies and research institutions working with sensitive data or needing to meet regulatory requirements.
Both GPU servers come with pre-installed NVIDIA drivers and CUDA toolkit. Choose between Linux (Ubuntu, Debian, CentOS, AlmaLinux) or Windows Server (surcharge). Unlimited traffic (fair use), DDoS protection and 24/7 monitoring are included. For maximum bandwidth, a 10 Gbit/s uplink is available as an option. Contract terms: 12, 24 or 36 months.
FAQ
A GPU server is a dedicated server with a powerful graphics card (GPU) optimised for parallel computations. Unlike conventional servers that rely solely on CPU power, GPU servers can process compute-intensive tasks such as AI training, 3D rendering or scientific simulations many times faster thanks to thousands of CUDA cores.
TFLOPS (Tera Floating Point Operations Per Second) indicates how many trillions of floating-point operations a GPU can perform per second – the higher, the faster AI models are trained or computations performed. vRAM (Video RAM) is the GPU's working memory. Large AI models and high-resolution 3D scenes require a lot of vRAM to be processed efficiently.
FireStorm offers two GPU configurations: The NVIDIA RTX 4000 SFF Ada with 20 GB GDDR6 ECC and 19.2 TFLOPS in the GPU Starter, and the NVIDIA RTX PRO 6000 Blackwell Max-Q with 96 GB GDDR7 and 125 TFLOPS in the GPU Pro. Both GPUs are dedicated and not shared.
GPU servers are suitable for: AI and machine learning training, LLM fine-tuning, inference (running AI models), 3D rendering and visualisation, video encoding and transcoding, scientific simulations (CFD, molecular dynamics), data analysis and big data, as well as CAD/CAE computations.
No, backup is not included by default with GPU servers. We recommend implementing your own backup strategies, e.g. via rsync, Borg Backup or cloud storage. On request, we can set up individual backup solutions.
Yes, you can choose between Linux (Ubuntu, Debian, CentOS, AlmaLinux) and Windows Server. Windows incurs a monthly surcharge for the licence. CUDA and NVIDIA drivers are pre-installed on both operating systems.