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AI SERVERS

AI & GPU Servers

Dedicated GPU-server options for LLM, image generation, RAG and data-processing workloads. GPU model, inventory, driver/framework versions and capacity are confirmed with a representative benchmark.

NVIDIA GPUAMD EPYCCUDA 12PYTORCHTENSORFLOWLLMSTABLE DIFFUSION
European Infrastructure
Frankfurt & Paris
Plan-Based Resources
Stated in technical table
24/7 Request Intake
Standard support · critical on-call only under product/SLA scope
Plan-Based Backups
Frequency and retention in plan

Plans

Billing period, discount and delivery model are stated on each plan card.

AI Workstation
Project quote
0
  • NVIDIA RTX A4000 (16 GB GDDR6)
  • AMD Ryzen 9 5950X (16c/32t)
  • 128 GB DDR4 ECC RAM
  • 2× 1.92 TB NVMe SSD
  • CUDA + PyTorch + TF preinstalled
Request a project quote
AI Studio (L40S)
Project quote
0
  • NVIDIA L40S (48 GB GDDR6, Ada)
  • AMD EPYC 7543 (32c/64t)
  • 256 GB DDR4 ECC RAM
  • 4× 3.84 TB NVMe SSD · 10 Gbps
  • LLM and multimodal workloads · benchmark required
Request a project quote
AI Enterprise (A100)
Project quote
0
  • NVIDIA A100 PCIe (40 GB HBM2e)
  • AMD EPYC 7763 (64c/128t)
  • 512 GB DDR4 ECC RAM
  • 8× 3.84 TB NVMe SSD · 25 Gbps
  • MIG (Multi-Instance GPU) support
Request a project quote
AI Top (H100)
Project quote
  • NVIDIA H100 PCIe (80 GB HBM3)
  • AMD EPYC Genoa (96c/192t)
  • 1 TB DDR5 ECC · 100 Gbps
  • Transformer Engine + FP8
  • Large-model workloads · benchmark and topology design
Get a Quote

Why AIOR AI Servers?

Total cost between hourly cloud GPU and monthly dedicated GPU depends on utilisation, data transfer, storage, management and commitment terms. The GPU model and software image available from AIOR are stated in the quote after current inventory and project compatibility are confirmed; not every model is assumed to be in stock at all times.

Which GPU for which workload?

GPU selection cannot be made from model parameter count alone. Weight precision and quantisation, context length, KV cache, batch size, training/inference method, multi-GPU topology and latency target all change memory needs. The candidate workload is benchmarked first, then the appropriate GPU, VRAM and scaling plan are selected.

Plan Comparison

FeatureAI WorkstationAI ProAI Studio (L40S)AI Enterprise (A100)AI Top (H100)
GPU
GPU modelRTX A4000RTX A5000L40SA100H100
GPU memory16 GB GDDR624 GB GDDR648 GB GDDR640 GB HBM2e80 GB HBM3
CPU & RAM
CPURyzen 9 5950XTR PRO 5965WXEPYC 7543EPYC 7763EPYC Genoa
vCore16c/32t24c/48t32c/64t64c/128t96c/192t
RAM (ECC)128 GB256 GB256 GB512 GB1 TB DDR5
Network & Storage
Storage2× 1.92 TB NVMe4× 1.92 TB NVMe4× 3.84 TB NVMe8× 3.84 TB NVMe16× 3.84 TB NVMe Gen4
Port speed1 Gbps1 Gbps10 Gbps25 Gbps100 Gbps

Frequently Asked Questions

Hourly or monthly rental?
Monthly dedicated and any hourly options are offered subject to inventory and provider terms. Cost comparison is made before ordering from actual utilisation, data transfer, storage, management and commitment period.
What's the minimum GPU rental period?
Minimum term, cancellation, refund and commitment discount vary by selected hardware and provider terms; they are shown in the quote and service terms before ordering.
Which frameworks come preinstalled?
CUDA/drivers plus PyTorch, TensorFlow, JAX, Transformers, vLLM, ONNX Runtime and container tooling can be installed for project compatibility. Exact versions are pinned through a dependency matrix and acceptance test.
I want multiple GPU nodes?
Multi-GPU or multi-node design depends on in-server interconnect, network fabric, NCCL compatibility, storage and framework support. NVLink availability depends on the model and chassis topology and requires technical validation before quote.
Which LLM models can I run?
Runnable model size depends on quantisation, context, batch size, KV cache, fine-tuning method and parallelism. No fixed capacity is promised until VRAM is calculated and a short benchmark is run using the model artifact and target throughput/latency.

Ready to get started?

Requests accepted 24/7 · standard support Mon–Fri 09:00–18:00 · critical on-call only under the applicable product or written SLA