Dedicated GPU Clusters for organizations building at scale

Deploy NVIDIA B200 and B300 infrastructure in UpCloud’s new flagship Nordic AI data center.

Reserve Dedicated GPU Cluster capacity in 8-, 16-, 32-, or 64-node configurations, with InfiniBand-enabled clustering, flexible commercial terms, and delivery from January 2027.

Starting from €3.99/hour per NVIDIA B200
With a 36-month commitment and partial prepayment

Reserve capacity

Talk to our AI infrastructure team

Joel, CEO & Founder

Dedicated GPU Clusters

NVIDIA B200 & B300 GPUs

Node Clusters: 8–64
InfiniBand: Enabled
Location: Flagship Nordic Data Center
Agreement Duration: 24 or 36 months
Delivery: January 2027

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Your Private AI infrastructure awaits

Our Dedicated GPU Clusters offer the ultimate combination of performance and privacy. It integrates the raw computational power of our dedicated NVIDIA GPU servers into our exclusive Private Cloud infrastructure. This creates a secure, single-tenant environment where you have full control over your hardware, data, and AI-driven future.

Reserve Capacity

Don’t compete for GPU capacity. Secure it.

For large AI workloads, finding individual GPUs is not enough.
You need confidence that the compute your roadmap depends on will be available, deployed together, connected with high-performance networking, and ready when your teams need it.

UpCloud’s new Nordic AI data center is designed for customers looking to secure substantial blocks of NVIDIA B200 and B300 capacity for training, inference, reasoning and other compute-intensive AI workloads.
Plan your infrastructure today and lock in dedicated capacity for delivery from January 2027.

Discuss your capacity requirements

European Sovereign AI Infrastructure

Built for serious AI workloads

Foundation model training

Scale distributed training across dedicated GPU infrastructure.

Fine-tuning and post-training

Give AI teams predictable compute for experimentation, fine-tuning, and model optimization.

AI inference at scale

Deploy dedicated capacity for high-throughput, latency-sensitive production workloads.

Reasoning and agentic AI

Build infrastructure for increasingly compute- and memory-intensive AI systems.

Private and sovereign AI

Run strategic AI workloads on dedicated Nordic infrastructure with a European cloud provider.

AI platforms and services

Create the infrastructure foundation for customer-facing AI products and internal AI platforms.

Large AI workloads depend on more than GPU performance.

Built to perform as one cluster

As training and inference scale across multiple nodes, the speed of communication between those nodes becomes increasingly important. UpCloud’s large-scale AI deployments are InfiniBand-clustering enabled, providing high-bandwidth, low-latency connectivity for demanding distributed workloads.

Scale workloads across nodes
Run distributed training and compute-intensive AI workloads across a larger pool of accelerators.

Reduce communication bottlenecks
Move data efficiently between nodes as workloads scale.

Increase infrastructure utilization
Build around an architecture designed to keep high-value GPU resources working effectively together.

Build around the capacity your workloads require.

Scale from 8 to 64 nodes

8-node cluster
A strong entry point for distributed AI workloads, large-scale fine-tuning, private AI platforms, and high-performance inference.

16-node cluster
More dedicated compute for larger models, growing datasets, and sustained production workloads.

32-node cluster
Designed for organizations running demanding distributed training and high-throughput AI platforms.

64-node cluster
Large-scale AI infrastructure for organizations that need substantial dedicated capacity for their most demanding workloads.

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Capacity with commercial flexibility

Large AI infrastructure is a long-term investment. The commercial model should reflect that.
Choose either a 24 or 36-month contract, with flexible payment structures available depending on your deployment.

Starting from €3.99/hour per B200 or €5.32 for B300

Available with a 36-month commitment and partial prepayment.

For exact pricing and configuration, our team will work with you to structure capacity, contract length, and payment terms around your requirements.

Request commercial terms

Plan your AI infrastructure with greater certainty

Securing capacity in advance gives your organization more than GPUs.
It gives you a predictable foundation for your AI roadmap.

Predictable delivery

Plan against a defined January 2027 deployment window.

Flexible commitments

Choose between 24 and 36-month contract structures based on your infrastructure strategy.

Flexible payment terms

Structure payment around your deployment, including partial prepayment options.

Scale from day one

Deploy 8, 16, 32, or 64 nodes as a coordinated cluster rather than assembling capacity incrementally.

Compare GPU models

GPU Model Specifications

UpCloud’s Dedicated GPU Servers offer a range of GPU models with the unique ability to scale the server and GPUs as needed.

	NVIDIA B200	NVIDIA B300
Primary use case	Trillion-parameter model inference, model training, running complex models in real-time	Advanced AI reasoning, large-scale model training and inference, agentic and multimodal AI, HPC and AI factory workloads
Architecture	Blackwell	Blackwell Ultra
GPU Memory	192GB HBM3e	288GB HBM3e
Memory Bandwidth	8.0TB/s	8.0TB/s
Peak compute	FP32 74.45 TFLOPS FP8 Tensor 9.00 PFLOPS	FP8 Tensor 9.00 PFLOPS FP4 Tensor 18.00 PFLOPS

GPU Server Hardware

NVIDIA B200

Generative AI, Large-Scale AI Model Training and Inference (LLMs), High-Performance Computing (HPC) simulations, Scientific Computing, and Data Analytics.

The NVIDIA B200 GPU, powered by the Blackwell architecture, is the world’s most powerful AI chip, designed to power a new era of computing with up to 4x faster training and 30x faster inference than previous generations.

NVIDIA B300

Advanced AI Reasoning, Large-Scale AI Model Training and Inference, Agentic and Multimodal AI, High-Performance Computing (HPC), Scientific Computing, and AI Factory deployments.

The NVIDIA B300 GPU, powered by the Blackwell Ultra architecture, is designed for the next generation of AI reasoning and large-scale accelerated computing. With up to 288 GB of HBM3e memory and 8 TB/s of memory bandwidth, B300 delivers 1.5x greater dense FP4 performance and 2x higher attention performance than B200, accelerating long-context models, complex inference, and training at data-center scale.

Ready for the Worlds

Built in the Nordics

Run your next generation of AI infrastructure from UpCloud’s flagship Nordic AI data center.
For European organizations, this provides a powerful alternative to building critical AI infrastructure around hyperscaler availability outside the region.
Combine next-generation NVIDIA accelerators with:

Nordic infrastructure

Run dedicated AI capacity from the Nordics.

European cloud expertise

Work with an independent European cloud provider.

High-performance networking

Build distributed workloads on InfiniBand-enabled cluster infrastructure.

Dedicated capacity

Secure infrastructure around your roadmap rather than competing for individual instances.

From capacity planning to deployment

Securing a large GPU cluster should be structured, predictable, and technically validated before commitment.

01 — Letter of Intent

Define your target capacity and signal your intention to reserve infrastructure for the January 2027 delivery window.

02 — Technical Validation

Work with UpCloud to validate your workload, GPU requirements, cluster size, networking, and infrastructure architecture.

03 — Contract

Agree on your final deployment, commercial structure, contract duration, and payment terms.
Choose from 24–36 month options, depending on your requirements.

04 — Delivery

Your dedicated AI infrastructure is prepared for deployment from January 2027.

Demand for next-generation AI infrastructure is growing quickly.

Secure your January 2027 AI capacity

If B200 or B300 capacity is part of your 2027 AI roadmap, now is the time to start planning the infrastructure behind it.
Tell us what you’re building, the scale you expect to reach, and the capacity you need.
We’ll help you validate the architecture and secure a deployment aligned with your requirements.

NVIDIA B200 and B300 Dedicated GPUs

✔️8-, 16-, 32- and 64-node configurations
✔️InfiniBand-enabled clustering
✔️Nordic location
✔️24–36 month contract options
✔️Flexible payment terms
✔️From €3.99/hour per B200*
✔️Delivery from January 2027

*Starting price of €3.99/hour applies to one NVIDIA B200 with a 36-month commitment and partial prepayment. Final pricing depends on configuration, contract term, capacity, and commercial structure.

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Dedicated GPU Cluster Key features & benefits

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5th gen AMD EPYC Turin hardware benchmark comparison - UpCloud.com

Uncompromised performance and privacy

Leverage the full potential of dedicated AMD EPYC 9575F Turin hardware and NVIDIA GPUs in an environment that is physically isolated for your exclusive use. Your data, models, and infrastructure remain yours, shielded from external threats and compliant with GDPR.

Dedicated NVIDIA GPUs: Guaranteed, consistent performance for AI/ML, LLM inference, and 3D rendering without shared hardware.

Physically isolated hardware: Your entire cloud runs on dedicated hosts, ensuring your data is secure and isolated.

MaxIOPS Block Storage: Industry-leading storage performance for your most demanding workloads.

Built for modern demands

Multi-workload acceleration

Render, analyze & transform

Supercharge your visual data workflows. From high-resolution video transcoding and analysis to advanced image recognition and manipulation, these GPUs provide the massive parallel processing power for demanding media tasks, accelerating production and enabling new creative possibilities.

Instant insights

Unlock real-time insights with our NVIDIA GPUs. Significantly accelerate the deployment of trained machine learning models, delivering low-latency inference for faster, more responsive AI-driven experiences.

Smarter language, fast!

Accelerate the understanding and generation of human language. These GPUs significantly speed up natural language processing (NLP) tasks like sentiment analysis, text summarization, and conversational AI, empowering you to build more sophisticated and efficient language-based applications faster.