Cloud GPU
Provision flexible GPU server capacity for development, experimentation and scalable AI services.
PRODUCT BENEFITS
Compute That Matches the Way Your AI Team Works
Choose flexible virtualized capacity or dedicated bare metal according to workload, isolation and operational needs.
Bare-Metal GPU
Use dedicated GPU infrastructure for demanding workloads and stronger resource isolation.
AI Workload Ready
Support model training, fine-tuning, inference and other accelerated computing workflows.
Simpler Operations
Move from infrastructure planning to deployment through a consistent service experience.
CORE CAPABILITIES
Two Deployment Models, One AI Computing Platform
Select the infrastructure profile that best fits each phase of the AI lifecycle.
FLEXIBLE CAPACITY
Cloud GPU
Provision GPU server environments for experimentation, engineering and production AI services.
- Flexible project environments
- Training and inference workloads
- Service-based operations
DEDICATED INFRASTRUCTURE
Bare-Metal GPU
Run demanding workloads on dedicated GPU infrastructure with stronger resource isolation.
- Dedicated physical resources
- Workload isolation
- Custom deployment requirements
GLOBAL INFRASTRUCTURE
The Global Network Behind Every Compute Deployment
1,500+
Global PoPs
Across core and emerging markets
60+
Countries & Regions
Connecting global demand
170+
Partner ISPs
Deep local interconnection
90+Tbps
Network Capacity
Reserved for traffic at scale
GPU OPTIONS
Choose the Right GPU for Every AI Workload
Configure everything from efficient single-GPU environments to large-memory enterprise GPU nodes as model scale and production needs evolve.
ENTERPRISE AI & VISUAL COMPUTING
RTX PRO 6000
Blackwell Server EditionBuilt for large-memory model inference, agentic AI, fine-tuning, scientific computing and demanding data-center visualization workloads.
96 GB GDDR7 ECC24,064 CUDAUp to 1.6 TB/s
HIGH-PERFORMANCE DEVELOPMENT & INFERENCE
GeForce RTX 5090
Blackwell ArchitectureA strong fit for single-GPU AI development, generative content, multimodal experimentation and cost-conscious inference workloads.
32 GB GDDR721,760 CUDA1,792 GB/s
NVIDIA reference specifications shown. Actual node configuration and regional availability may vary.
Elastic Cloud GPU
Flexible GPU server capacity for changing project and application demand.
Dedicated Bare Metal
Physical GPU resources reserved for a single workload environment.
Development Environments
Create repeatable compute environments for AI engineering teams.
Training Workloads
Run compute-intensive model training and adaptation workflows.
Inference Deployment
Host model-serving workloads that require accelerated compute.
Workload Isolation
Choose deployment models according to security and isolation requirements.
HOW IT WORKS
A Direct Path from Capacity to Workload
Choose the deployment model, provision an environment and move AI workloads into operation.
WORKLOAD LAYER
1AI Engineers
2ML Pipelines
3Schedulers
Capacity request
COMPUTE CONTROL PLANEAI Computing
ProvisioningRuntime ImagesNetwork & StorageLifecycle Control
ACTIVE POLICY PATH99.99%
Scheduled capacity
GPU RESOURCE POOLS
ACloud GPU
BBare-Metal GPU
CAI Workloads
PLATFORM OPERATIONS
TrainingFine-TuningInferenceMonitoring
Choose
Select cloud GPU or bare-metal GPU.
Provision
Create an environment for the workload.
Deploy
Run training, tuning or inference.
Operate
Manage workload growth and lifecycle changes.
APPLICATION SCENARIOS
Built for the AI Workloads That Matter
Use AI Computing wherever performance, choice and production control are essential.
Model Training
Run compute-intensive training projects on GPU infrastructure.
Match infrastructure to the training plan
Fine-Tuning
Adapt models for domain and application requirements.
Create dedicated project environments
AI Inference
Deploy accelerated model-serving workloads.
Use GPU capacity for production delivery
AI Engineering
Give teams repeatable GPU development environments.
Shorten the path from experiment to serviceWHY EDGENEXT AI
Simplify the Hard Parts of Production AI
Capacity Procurement
A service-based model reduces the work of sourcing and preparing GPU infrastructure.
Deployment Trade-Offs
Cloud and bare-metal options support different needs for flexibility and isolation.
Operational Overhead
A consistent platform experience simplifies infrastructure lifecycle management.