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19 Years of Experience

GPU-Based AI Infrastructures

We plan GPU-based server infrastructure for your AI and machine learning projects with a project-based procurement model, perform installation and provide hosting/operation support in Netinternet data center. We do not offer GPU server rental service.

GPU AI Infrastructures

GPU-Based

AI Server Solutions

We design NVIDIA GPU supported server architecture for your deep learning, large language models (LLM), computer vision and data analytics projects. We determine the right configuration during the procurement process, perform installation and provide hosting + operation support in our data center.

Project-Based Discovery Request →
Note: GPU rental service is not offered within the scope of this page. The solution proceeds with a procurement + hosting model.
GPU AI Server
Important Notice
Netinternet does not provide GPU server rental within the scope of this service. We support the procurement of suitable GPU hardware through needs analysis, perform installation and deliver it operational with hosting / network / power / operation infrastructure in our data center. You can also host your existing GPU server in our data center and we can provide support in operational processes.

Use Cases

AI workloads supported by our GPU-based infrastructure

Large Language Models (LLM)

Training and inference of large language models like GPT, LLaMA, Mistral. Fine-tuning, RAG systems and chatbot infrastructures.

Computer Vision

Object detection, image classification, face recognition, medical image analysis. YOLO, ResNet, ViT model training.

Data Analytics & ML

Accelerated analysis on large datasets, recommendation systems, anomaly detection, forecasting models and GPU-accelerated data processing with RAPIDS.

Generative AI

Stable Diffusion, DALL-E, Midjourney style image generation. Voice synthesis, music generation and video creation infrastructures.

Autonomous Systems

Autonomous vehicle simulations, robotic control, reinforcement learning and real-time decision support systems.

Scientific Computing

Molecular simulation, climate modeling, genome analysis, physics simulations and CUDA-based high performance computing (HPC).

Project-Based GPU Configuration

Configure the GPU server suitable for your needs, let us prepare a custom quote

CONFIGURATION SUMMARY

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GPU & Hardware Selection

Project Details

Contact Information

Why Netinternet GPU?

AI Infrastructure Advantages

NVIDIA GPU
NVIDIA Ecosystem
01

Latest Generation NVIDIA GPUs

We determine suitable NVIDIA GPU options based on your project needs, plan the architecture before procurement and manage installation/integration processes. We build the right infrastructure for AI workloads with multiple GPUs per server, high I/O and appropriate network design.

  • 80GB HBM3 memory (H100)
  • Scaling up to 8x GPU per server
  • FP16, BF16, INT8, FP8 precision support
Project-Based
Project-Based
02

Procurement + Installation Focused Project Design

Instead of ready-made packages, the appropriate hardware configuration is determined based on your workload (training/inference), dataset size, GPU memory needs and target performance values. This way, the right investment is planned without unnecessary costs.

  • Needs analysis and consulting
  • Correct configuration before procurement
  • Installation, testing and commissioning
Infrastructure
Enterprise Infrastructure
03

Enterprise-Level Infrastructure

We create GPU server environments commissioned and operational on Netinternet data center infrastructure, with CUDA/cuDNN/TensorRT and AI framework installations upon request. We can configure to support modern working models like Docker/Kubernetes.

  • PyTorch, TensorFlow, JAX ready environment
  • Docker & Kubernetes support
  • Root access via SSH, full control

Technical Specifications

Standard components we offer within installation and hosting scope

Software Environment

CUDA Toolkit, cuDNN, TensorRT, NCCL installation is done upon request. Frameworks like PyTorch, TensorFlow, JAX, HuggingFace are prepared according to project needs.

Architecture Design

GPU count, NVMe storage, PCIe/NVLink topology and network capacity are planned according to your dataset and training/inference targets.

24/7 Operational Support

24/7 support for hosting, network, power, hardware monitoring and system operation processes. Consulting on AI-side installation and basic optimization.

Distributed Training Setup

Infrastructure planning and installation support for multi-GPU and multi-node architectures. We prepare your training infrastructure with the right topology and network design for approaches like DeepSpeed, FSDP.

Dramatically reduce your training time with Data Parallelism, Model Parallelism and Pipeline Parallelism strategies.

Inference Infrastructure

We provide inference environment setup and performance-focused configuration support with components like TensorRT, vLLM, ONNX Runtime.

Reduce your costs with model quantization (INT8/FP8), batched inference and KV-cache optimizations.

GET QUOTE

Frequently Asked Questions

Frequently wondered topics about GPU-based AI infrastructures

Do you rent GPU servers?

No. Netinternet does not provide GPU server rental within this service scope. Our model works by properly planning the GPU hardware suitable for your needs during the procurement process, installing the server and commissioning it with hosting / network / power / operation support in Netinternet data center. You can also host your existing GPU server in our data center.

We determine the most suitable GPU model together based on project goals. During procurement, options such as A100, H100, L40S, RTX A6000 are evaluated and the final configuration is created based on availability. Our goal is not the most expensive GPU but the right cost-performance balance for your workload.

Instead of ready-made packages, your workload is analyzed and GPU count, CPU/RAM, NVMe storage, network capacity and cooling/power requirements are determined. This way, the architecture that will provide the performance you need is planned without unnecessary investment.

After needs analysis, we determine the appropriate hardware, evaluate procurement options together and perform technical validation before purchase. Then we complete the server installation in the data center, basic software installation and tests to make it production-ready. We can also provide project-based support for architectural consulting.

Upon request, basic components can be prepared at server delivery: NVIDIA driver, CUDA Toolkit, cuDNN, NCCL are installed with versions suitable for project requirements. Frameworks like PyTorch, TensorFlow, JAX and HuggingFace ecosystem can also be installed with desired versions. Configuration can also be done for Docker-based working model and image standardization.

Yes, if needed, we provide infrastructure design and installation support for multi-GPU architecture and distributed training. GPU-GPU communication topology, NVMe throughput, network capacity and synchronization requirements are planned according to project goals. Components like NVLink / high bandwidth network are optional depending on configuration and procured hardware.

Rack space, power, cooling, network access, hardware monitoring and physical security layers are provided in Netinternet data center. Network options such as internet access, private VLAN, BGP can be planned according to needs.

Within 24/7 operational support; we provide support for hardware monitoring, failure management, network access, basic operating system processes and GPU driver/CUDA version compatibility. Model optimization or code-level development is handled separately as consulting based on project scope.

For a quick discovery, the following information is sufficient: model type (LLM/CV/GenAI), training or inference, target GPU memory needs, dataset size, concurrent usage expectation, storage capacity and budget range. With this information, we can quickly create a GPU count, CPU/RAM, NVMe and network design proposal.