Cloud GPUs provide flexible, scalable access to advanced graphics processing power. By tapping into cloud-hosted GPUs, organizations can accelerate everything from machine learning model training to 3D visualizations, bypassing the high costs and capacity constraints of on-premises GPU clusters.
But with a growing array of cloud GPU options now available, selecting the right platform can feel overwhelming. In this comprehensive guide, we evaluate the top contenders across key criteria to help you find your best fit.
An Introduction to Cloud GPUs
Before diving into the offerings from specific providers, let‘s briefly examine what cloud GPUs are, their benefits, leading use cases, and how they achieve accelerated computing capabilities:
What Are Cloud GPUs?
Cloud GPUs refer to GPU computing resources hosted in the cloud that users can leverage on-demand. Leading cloud providers like AWS, GCP, and Azure allow customers to launch GPU-powered virtual machines, harnessing graphics acceleration to handle workloads locally or in the cloud.
Some of the most popular GPUs offered across cloud platforms include:
- NVIDIA Tesla V100 – Optimized for AI, ML, and HPC workloads
- NVIDIA T4 – Cost-effective performance for inference
- AMD Radeon Instinct – Accelerates HPC applications
Why Cloud GPUs?
There are several compelling benefits to using cloud-based GPU resources rather than on-premises GPUs:
- Cost Savings – Pay only for what you use rather than purchasing full-capacity GPU clusters
- Scalability – Scale GPU resources up and down to meet changing demands
- No Specialized Hardware – No need to invest in and maintain GPU servers in-house
- Global Availability – Leverage GPUs closest to your data and end users
Top Cloud GPU Use Cases
Common workloads powered by cloud GPU instances include:
- AI Model Training – Accelerate deep learning with massive parallel processing
- Machine Learning – Shorten model iteration cycles with rapid prototyping
- 3D Rendering – Tap into tremendous graphics performance
- Video Encoding – Speed up production and compression
- Computational Finance – Run complex quantitative models
- Molecular Modeling – Simulate protein dynamics
Achieving Accelerated Computing with GPUs
To understand the full capabilities of cloud GPU offerings, it helps to examine how GPUs achieve accelerated performance in the first place:
GPU Architecture
GPUs contain up to thousands of compact processing cores designed specifically for highly parallel computation. Many GPUs also integrate specialized components like:
- Tensor Cores – Hardware designed to speed up deep learning neural network calculations
- RT Cores – Accelerate ray tracing for advanced 3D imaging effects
- NVLink – Interconnect allowing multiple GPUs to pool resources
This architecture empowers GPUs to handle workloads with vast amounts of parallelism far better than general-purpose CPUs.
Virtualized GPU Sharing
In a cloud environment, hardware virtualization now allows subdividing a physical GPU into isolated “virtual GPUs”. By carving up the resources into separate GPU acceleration slices, multiple users can share access to a single physical card.
Advanced GPU sharing will maximize utilization for cloud providers. But full access to dedicated, non-virtualized GPU hardware may yield the highest performance for some applications.
Now let’s compare offerings from the top cloud GPU providers.
AWS Cloud GPUs
Amazon Web Services offers one of the widest selections of GPU instance types across global regions.
GPU Options
AWS provides diverse NVIDIA GPUs via EC2, including:
- Tesla V100
- T4 Tensor Core
- M60
- P100
- K80
Newer specialized instances like Inf1, G4ad, and P4de are optimized specifically for machine learning acceleration.
For example, the P4de houses up to 8 A100 Tensor core GPUs with 400 Gbps network bandwidth, making it well-suited to scale ML training.
Use Cases
The extensive range of GPU configurations makes AWS a versatile option for:
- Deep learning and model training
- Graphics-intensive applications
- HPC workloads at scale
- Real-time video processing
From cloud gaming engines to computational drug discovery, the array of use cases is vast.
Provisioning & Management
Users can launch GPU instances via the AWS Management Console, CLI, CloudFormation scripts, or directly integrate infrastructure-as-code tools like Terraform.
GPU monitoring is available through Amazon CloudWatch for tracking utilization, load, and other metrics.
Pricing
AWS GPU pricing follows standard EC2 instance billing, charging per hourly usage with options like Savings Plans to reduce costs for steady-state workloads.
Estimated price per GPU hour ranges from:
- $0.90 – $14 for on-demand
- $0.60 – $9 for 1 yr Reserved Instances
- $0.45 – $12 for Savings Plans
Savings Plans can cut hourly costs by up to 66% for consistent usage.
Key AWS GPU Strengths:
- Massive selection of GPU options and instance sizes
- Tight integration with other AWS services
- Global infrastructure
- Sophisticated pricing model optimizations
Optimizing AWS GPU Costs
For ML workloads with occasional spikes in training demands, Savings Plans boosted by EC2 Spot Instances can minimize costs.
Optimize multi-node model training atop AWS infrastructure with placement groups and networking enhancements like Elastic Fabric Adapter (EFA).
Tools like AWS Batch simplify scheduling parallel jobs across GPU clusters.
Google Cloud Platform GPUs
GCP Cloud GPUs unlock graphics acceleration for VMs running on Google Compute Engine (GCE).
GPU Options
Google Cloud offers a smaller but potent arsenal of NVIDIA GPUs, like:
- A100
- T4
- P4
- P100
- K80
GPU type availability zones vary across regions – be sure to validate regional capacity for your workload needs.
Use Cases
Ideal workloads include:
- AI and ML
- Scientific computing
- Visualization
- Graphics/Video rendering
Specialized VMs have expanded memory, vCPUs, and NVMe SSD caching to feed data-hungry applications.
Provisioning & Management
Users can launch predefined GPU instance templates through GCP Console or gcloud command line. Advanced users can also craft customized configurations.
Integrate infrastructure deployment into CI/CD pipelines via Terraform, Deployment Manager, or other GCP SDKs.
GPU monitoring is available via Cloud Monitoring for tracking usage metrics.
Pricing
Hourly billing applies, with committed use discounts up to 57% over 1-3 years via GPU reservations.
Estimated price per GPU hour ranges from:
- $0.45 – $2.48 on-demand
- $0.35 – $1.28 for reserved capacity
Google also offers preemptible instances at up to 80% discounts – ideal for fault-tolerant batch workloads.
Key GCP GPU Strengths:
- Deep integration with Google ML services
- Customizable VM configurations
- Steep discounts for reserved capacity
- Preemptible VMs for batch/fault-tolerant workloads
Optimizing GCP GPU Costs
Use committed-use reservations, preemptible VMs, sole-tenant nodes, and quotas best practices to minimize costs.
Build multi-node distributed training atop VPC networks rather than public IP addresses.
Schedule jobs optimally using the Workload Manager for ML.
Microsoft Azure N-Series GPUs
Microsoft Azure provides purpose-built N-Series VMs powered by NVIDIA GPUs.
GPU Options
Azure offers fewer GPU models than AWS and GCP, focusing on:
- Tesla V100
- Tesla M60
- Quadro vDWS
Most configurations supply expanded vCPU, memory, temporary storage for intensive workloads.
Use Cases
Target applications include:
- Deep learning
- Model training
- Inferencing
- Virtual workstations
Azure tailors N-Series specifically for graphics, visualization, encoding, VDI, engineering simulations and more.
For example, NVv4 VMs house V100 GPUs well-suited for rendering and video editing. While NDv2 GHz editions pack 8 V100s into a multi-node ready VM for ML training.
Provisioning & Management
Users can deploy N-Series VMs with the Azure Portal, CLI, Terraform, or other SDKs. Capabilities like auto-scaling, load balancing ease cluster management.
Monitoring is available through Azure Monitor with 35+ predefined metrics on GPU usage, memory, and more.
Pricing
Azure charges pay hourly VM rates depending on the N-Series family and configuration selected.
Estimated price per GPU hour ranges from:
- $1.65 – $3.60 per hour on-demand
- $1.00 – $2.50 with 1 or 3 year RI discounts up to 72%
Azure Hybrid Benefit can trim license costs further for existing Windows Server or SQL Server license holders.
Key Azure GPU Strengths:
- Strong ML development environment
- Targeted VMs for AI/DL acceleration
- Generous RI discounts
- Azure Hybrid Benefit savings
Optimizing Azure GPU Costs
Combine Azure Hybrid Benefit discounts with Reserved Instances to compound savings.
Use Azure CycleCloud to orchestrate N-Series clusters along with Managed Identity authentication.
Schedule GPU-powered jobs efficiently leveraging Azure Batch.
Paperspace Gradient
Paperspace Gradient provides dedicated GPU infrastructure for development teams.
GPU Options
Available enterprise GPU choices encompass:
- NVIDIA Quadro RTX 6000
- NVIDIA Quadro RTX 8000
- NVIDIA Tesla T4
- NVIDIA Tesla P100
With high-memory Quadro RTX8000 models packing 48GB GPU RAM to feed data-hungry models.
Use Cases
Well-suited for organizations running:
- Deep learning
- 3D/Video rendering
- Data science
- Gaming/Simulation engines
Fluent project collaboration tools aid graphics production pipelines.
Provisioning & Management
A web console and API automates Gradient notebook deployment across public or private infrastructure.
Centralized monitoring and administration improves visibility and unifies management across a team.
Pricing
Fine-grained pay-per-second billing granularity aligns costs closely to actual usage rather than hourly chunks. Autoscaling further optimizes resources to workload shape.
Starting from:
- $0.59 per GPU hour for T4 model
- $2.15 per GPU hour for Quadro RTX 6000
Volume discounts available for larger allotments and longer periods.
Key Paperspace GPU Strengths:
- Team collaboration tools
- Unified control plane
- Granular per-second billing
Paperspace makes it easier for project teams to share access to accelerated computing capacity.
Vast.ai
Vast.ai grants affordable access to consumer GPUs by aggregating and renting capacity from an ecosystem of hardware owners.
GPU Options
Vast.ai allocates consumer NVIDIA cards on-demand like:
- GeForce RTX 3090
- GeForce RTX 3080 Ti
- GeForce RTX 3080
- GeForce RTX 3070 Ti
- GeForce RTX 2080 Ti
Spanning various memory sizes up to 24GB.
AMD Radeon options like the RX 6900 XT also available on limited nodes currently.
Use Cases
The economic pricing makes vast.ai accessible for:
- Personal ML experiments
- Game modding
- Video editing
- Cryptocurrency mining
Augmenting occasional workstation-level projects rather than full-scale development.
Provisioning & Management
Users browse and select their preferred GPU type via the Vast.ai rental marketplace portal.
SSH access then provides low-level control as if working on a dedicated server or bare-metal machine.
Pricing
The peer-to-peer rental model results in competitive pricing from $0.20 per GPU hour for older Geforces up to $3.00 per hour for the high-end RTX 3090s.
Rental duration discounts further reduce hourly rates for longer-term reservations.
Key Vast.ai GPU Strengths:
- Direct SSH access to dedicated hardware
- Latest consumer GPU options
- Low hourly rates – great personal value
Vast.ai drops the barrier to leverage GPUs for personal projects.
For cryptocurrency miners, Vast.ai also provides transparent profitability tracking based on real-time coin rates and mining luck. Miners can view expected break-even timeframes to gauge opportunities.
Choosing the Right Platform
With the wide range of cloud GPU options now available, focus decisions based on:
Performance Needs – Carefully match GPU capability, cores, memory to application requirements. Overprovisioning carries a heavy price tag.
Billing Model – Balance hourly flexibility vs. discounts for steady-state commitments based on workload patterns.
Ecosystem Integration – Tight integration with complementary cloud services may boost productivity.
Specialization – Seek targeted solutions for specialized needs like media production, finance, life sciences
Budget – Less expensive consumer GPUs work for personal experimentation, but fall far short of enterprise capabilities.
Using these decision factors will lead you to the ideal cloud graphics acceleration solution.
The Future of Cloud GPUs
As AI, ML, Metaverse applications transform industries, expect relentless innovation in cloud GPU offerings seeking to dominate surging demand.
Key developments on the horizon include:
- New high-memory and inference-optimized GPUs
- Advanced virtualization for sharing and isolation
- Tighter coupling with high-speed, low-latency networking
- Usage-based discounted pricing models
- Streamlined provisioning workflows
- Integration as code enhancements
For computationally-intensive domains from personalized medicine to autonomous vehicles, cloud GPUs now offer a versatile pathway to harness state-of-the-art graphics acceleration while minimizing costs.
By matching specific application needs to the strengths of each leading provider, engineering teams can productively tap into advanced computing capabilities to power new innovations.
