GPU Cloud
AI/GPU as a Service Platform
Scale Your Enterprise AI Infrastructure with Confidence
GPU Cloud is an AI/GPU as a Service platform designed for enterprise AI projects, running in data centers located in Türkiye.
OVERVIEW
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NVIDIA Enterprise GPU
GPU platform optimized for modern AI projects -
AI/GPU as a Service
Scalable GPU infrastructure for enterprise AI workloads -
Data Centers Located in Türkiye
Low-latency, locally hosted AI infrastructure -
Dedicated & Shared GPU
Flexible GPU options tailored to your workload -
Kubernetes & VM Support
Virtual machine and Kubernetes-based AI environments -
Self-Service Management
Easy resource management through the portal and API
Manage Your AI Projects from a Single Platform
Enterprise AI projects are about more than just GPU resources. A successful AI platform must manage data preparation, model development, training, performance validation, inference, and production processes together.
GPU Cloud offers an integrated AI Platform approach designed to help organizations manage the entire AI lifecycle end to end. Instead of switching between different tools, you can manage all your processes through a single platform.
AI Platform Lifecycle
1
Data Preparation
2
Model Development
3
Model Training
4
Fine-Tuning
5
Validation
6
Benchmark
7
Inference
8
API Deployment
9
Monitoring
10
Optimization
11
Production
Platform Features
All the capabilities you need for enterprise AI projects, on a single platform.
AI Compute
Run AI, HPC, and data-intensive workloads on high-performance NVIDIA GPU infrastructure.
AI Training
Train Foundation Models, LLMs, and enterprise AI models on scalable GPU clusters.
Inference
Run real-time AI services in production with low latency.
API and Self-Service Management
Easily manage and automate your GPU resources with portal and API support.
Virtual Machines and Kubernetes
Run your AI applications on GPU-enabled virtual machines or Kubernetes clusters with an architecture tailored to your needs.
AI Benchmarking
Identify the optimal infrastructure by analyzing performance and costs across different GPU architectures.
AI Factory
Manage the AI lifecycle on a single platform, from development to production.
Agentic AI
Create environments that support next-generation AI Agent systems and Tool Calling architectures.
Edge AI
Run real-time AI applications as close as possible to the user or data source.
AI Workloads
GPU Platform Optimized for Enterprise AI Workloads
Not every AI project requires the same infrastructure.
Chatbot applications, Large Language Models (LLMs), computer vision systems, and high-performance computing (HPC) workloads require different GPU architectures, memory capacities, and performance levels.
GPU Cloud provides the performance and flexibility you need through a single platform, with NVIDIA GPU infrastructure optimized for different AI workloads.
Large Language Models (LLM)
High-performance GPU infrastructure for Foundation Model development, Fine-Tuning, RAG, and Production Inference.
Generative AI
Run text, image, video, and multimodal AI applications on scalable GPU clusters.
Computer Vision
Support vision-based AI applications such as OCR, object detection, quality control, video analytics, and medical image processing.
Agentic AI
Build next-generation AI applications with AI Agents, Tool Calling, and multi-agent systems.
HPC and Scientific Computing
Use GPU-accelerated infrastructure for simulations, engineering analyses, and scientific research requiring intensive computational power.
GPU Rendering ve Görsel Hesaplama
GPU Cloud provides high-performance GPU infrastructure not only for AI projects, but also for rendering, video processing, and professional visual production workloads.
Real-World Use Cases
Support Enterprise AI Projects.
GPU Cloud is designed to support AI applications running in production environments across various industries.
Finance
Fraud Detection and Risk Analysis
Run AI models for real-time fraud detection, risk analysis, and anomaly detection on low-latency GPU infrastructure.
Platform Capability
- Real-Time Inference
- Scalable GPU Resources
- Türkiye-Located Data Processing
Media and Content Creation
Rendering and Visual Generation
Manage GPU-accelerated rendering, video processing, and generative AI applications on high-performance GPU infrastructure.
Platform Capability
- GPU Rendering
- Video Processing
- Generative AI
- Flexible GPU Resources
Healthcare
Medical Image Analysis
Develop AI models that process high-resolution medical images on secure and scalable infrastructure.
Platform Capability
- Computer Vision
- GPU Acceleration
- Inference
Government
Big Data Analytics and Decision Support Systems
Run big data analytics and AI-powered decision-making systems for public institutions on infrastructure located in Türkiye.
Platform Capability
- Big Data Analytics
- Natural Language Processing
- Local Data Processing
Defense
Critical AI Workloads
Create isolated AI environments for simulation and image analysis.
Platform Capability
- Air-Gapped AI
- Dedicated GPU
- Isolated Workspaces
Telecommunications
Network Analytics
Run grid optimization, traffic analysis, and predictive maintenance applications on GPU-accelerated AI infrastructure.
Platform Capability
- Edge AI
- AI Analytics
- Real-Time Processing
Retail and E-Commerce
Recommendation Engine
Train recommendation systems, customer segmentation, and demand forecasting models on scalable GPU infrastructure.
Platform Capability
- AI Training
- Inference
- Big Data Analytics
ENTERPRISE GPU PORFÖYÜ
Choose the Right GPU Architecture for Your Workload
Different AI projects benefit from different GPU architectures.
GPU Cloud offers options tailored to different performance levels, memory capacities, and use cases.
| GPU Model | AI Workload | Recommended Use |
|---|---|---|
| NVIDIA T4 | Real-Time Inference | Chatbots, API Services, Video Analytics, Edge AI |
| NVIDIA A30 | AI Training & HPC | Data Analytics, Scientific Computing, Enterprise AI |
| NVIDIA L40S | Multimodal AI | Computer Vision, Rendering, Video Processing, Generative AI |
| NVIDIA RTX Pro 6000 | Professional GPU Workloads | Content Creation, Graphics-Intensive AI, Edge AI |
| NVIDIA H100 | Foundation Model Training | Large-Scale AI Training, HPC, AI Factory |
| NVIDIA H200 | LLM & Reasoning | Large Language Models, High-Memory AI, Production Inference |
Note: Available GPU models may be updated based on inventory availability. Different GPU architectures can also be evaluated upon request.
Supported GPU Technologies
- NVIDIA CUDA
- Multi-Instance GPU (MIG)*
- GPU Passthrough
- Virtual GPU (vGPU)*
- NVLink*
- NVIDIA Container Toolkit
Note: Available GPU models may be updated based on inventory availability. Support availability may vary depending on the GPU model and architecture.
Need help choosing the right GPU?
Our AI experts provide technical consulting to help you determine the GPU architecture best suited to your use case.
AI BENCHMARKING
Determine the Right GPU Architecture for Your Workload
Not every AI model delivers the same performance on every GPU.
GPU Cloud conducts benchmark tests across different GPU architectures to help you determine the optimal balance of performance and cost for your model.
Benchmark studies enable the comparison of metrics such as training time, inference performance, memory utilization, and total cost of ownership (TCO), helping identify the GPU architecture best suited to your workload.
Benchmark results help you evaluate performance, cost, and resource utilization together when selecting the right GPU.
Benchmark Scope
AI Model Training Time
Time to First Token (TTFT)
Inference Latency
Token Generation Speed (Tokens/sec)
GPU Utilization Rate
Throughput Analysis
Memory Utilization
Performance / Cost Comparison
Sovereign AI Infrastructure
Bring Your AI Projects to Life with Confidence
In enterprise AI projects, having access to high-performance GPU resources alone is not enough. Where data is processed, how the infrastructure is managed, and whether operations can be securely maintained are just as critical as performance.
GPU Cloud helps organizations securely bring AI projects to life, particularly in regulated industries, by providing architectures that support data residency, regulatory requirements, and enterprise security policies.
Value
Description
Türkiye-Located AI Infrastructure
Run your AI workloads in local data centers.
Data Sovereignty
Manage your critical data on architectures of your choice.
Low Latency
Deliver optimized performance for users and systems within Türkiye.
Flexible Deployment Models
Create Dedicated, Shared, and Kubernetes-based GPU environments.
Enterprise Security Approach
Design infrastructure that supports security and operational requirements.
Scalable AI Platform
Easily scale your projects according to your needs.
Why DT Cloud?
In enterprise AI projects, choosing the right technology is just as important as choosing the right partner.
GPU Cloud combines modern AI infrastructure with an enterprise-grade operational approach and expert technical support to help you bring your AI projects into production faster.
Value
Description
AI/GPU as a Service
Scalable GPU infrastructure for enterprise AI workloads
NVIDIA Enterprise GPU Portfolio
GPU options optimized for different AI projects
Dedicated and Shared GPU
Flexible usage models tailored to your workload
Self-Service Portal and API
Easily manage and automate your resources
Data Centers Located in Türkiye
Low latency and operational control with local infrastructure
Expert Technical Support
Architecture consulting and technical expertise
AI Platform
Manage the AI lifecycle on a single platform
Frequently Asked Questions
No. In addition to AI projects, GPU Cloud can be used for rendering, video processing, scientific computing, and other GPU-accelerated high-performance workloads.
Yes. You can scale your GPU resources up or down based on your workload requirements.
Yes. GPU Cloud supports containerized AI applications on GPU-enabled Kubernetes clusters.
Our expert team can help you determine the right GPU architecture based on your workload requirements.
Yes. GPU Cloud conducts benchmark tests across different GPU architectures to help optimize performance and cost.
GPU Cloud provides infrastructure that supports CUDA-based GPU workloads and applications running on PyTorch, TensorFlow, and other widely used AI frameworks.
Your GPU options can be selected as either Dedicated or Shared, depending on the performance and isolation requirements of your workloads.
Our expert team helps you determine the most suitable GPU architecture by evaluating your model size, dataset, performance requirements, and budget together.
For more information, please fill out the form and we will call you.
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Copyright ©2025 DT. All Rights Reserved.