GPU Server

Rent a Dedicated GPU Server — RTX 4090 & 5090 in the EU

Rent a dedicated RTX 4090 or RTX 5090 server in our Frankfurt data center — single-tenant, at a fixed monthly price, with no hourly billing and no preemption . GDPR-compliant, EU-hosted, backed by human support.

Request a GPU server

Most GPU offers on the market bill by the hour or minute and run as preemptible spot instances — cheap on paper, but unpredictable for budgets and risky for long training jobs. Bthorio takes the opposite approach: you get a whole GPU dedicated to you, for a fixed monthly price, in the EU. No sharing, no interrupted runs, no bill that surprises you at month end.

GPU servers compared

The honest comparison: where Bthorio wins — and where hourly cloud GPUs or hyperscalers still make sense.

Bthorio dedicated GPU vs. typical alternatives
FeatureBthorioHourly cloud GPUHetzner GPUHyperscaler (AWS/GCP)
BillingFixed monthlyPer hour / minuteMonthlyPer hour
PreemptionNeverCommon (spot)NoOn spot instances
GPU allocationDedicated, single-tenantOften shared / vGPUDedicatedShared or dedicated
Current modelsRTX 4090 & 5090VariesOlder datacenter GPUA100/H100 (expensive)
LocationFrankfurt, DEMostly USGermany/FinlandGlobal
Data privacyGDPR, EU residencyOften US CLOUD ActGDPRVaries
Support24/7 engineersTicket / communityTicketEnterprise (paid)

Which GPU server fits your workload?

For most LLM inference, fine-tuning and rendering tasks the RTX 4090 with 24 GB VRAM (from €399/month) is plenty. If you need larger models, more VRAM or the Blackwell generation, choose the RTX 5090 with 32 GB VRAM . For AI/LLM deployments specifically we have a focused GPU server for AI page with guidance.

  • LLM inference & self-hosting (Ollama, vLLM, TGI): a dedicated GPU instead of shared cloud — no cold start, no preemption.
  • Fine-tuning smaller models & LoRA training: predictable budget instead of hourly roulette.
  • Stable Diffusion / ComfyUI / Flux: a full GPU for image and video generation.
  • Rendering, video encoding, scientific computing: CUDA and Tensor Core acceleration without shared resources.

What every GPU server includes

  • A whole RTX 4090 (24 GB) or RTX 5090 (32 GB) — dedicated, not shared
  • 128 GB DDR4 (4090) or 96 GB DDR5 (5090) system memory
  • 4 TB local NVMe for datasets and checkpoints
  • 1 Gbit uplink with fair-use traffic
  • Frankfurt am Main, carbon-neutral data center
  • Root access, free choice of OS, drivers & CUDA version

Frequently asked questions