Cloud graphics processing units (GPUs) allow anyone to harness tremendous parallel processing power on demand, over the internet. Instead of investing in high-end desktop hardware, you can offload intensive computational work like 3D rendering or machine learning model training to the cloud.
Top providers offer instant access to thousands of cutting-edge GPU cores plus terabytes of memory and storage. This guide compares 11 leading solutions available in 2023 based on performance, pricing and ease of integration.
Why Cloud GPUs Are Taking Over High-Performance Computing
The market for cloud-based graphics processing is exploding. According to MarketsandMarkets research, it will grow sixfold from $3 billion in 2022 to over $18 billion by 2027. What‘s driving adoption?
GPUs excel at massively parallel tasks like running deep neural networks for AI or processing graphics for VR/AR apps. Transferring these workloads from expensive on-premise servers to instantly scalable cloud infrastructure makes the power accessible for organizations of any size.
Cloud GPUs also align with larger trends towards cloud computing, AI acceleration and software-defined infrastructure. Let‘s examine some specific benefits:
Lower Total Cost of Ownership
- No large upfront capital investment in GPU servers
- Pay based on hourly or monthly usage rather than fixed hardware costs
- Savings from consolidation compared to on-prem data centers
Increased Agility
- Scale GPU capacity up and down almost instantly based on workload
- Accelerate time-to-market for GPU-powered initiatives
- Easy experimentation speeds R&D and innovation
Enhanced Productivity
- Eliminate time spent maintaining GPU infrastructure
- Leverage optimized environments for AI and graphics workloads
- Focus engineering resources on core products rather than supporting hardware
For today‘s GPU-hungry applications like autonomous driving, precision medicine and real-time analytics, cloud services offer unmatched flexibility.
Next let‘s dig into the top 11 providers vying for leadership in this booming market.
Top 11 Cloud GPU Providers Compared
Here is an overview of the leading cloud graphics processing solutions along with their target use cases:
| Cloud GPU Provider | Description | Use Cases |
|---|---|---|
| AWS Cloud GPUs | Broad selection of NVIDIA GPUs integrated with AWS cloud services | Machine learning, rendering, HPC |
| Microsoft Azure N-Series VMs | Azure instances featuring GPUs from NVIDIA and AMD | AI, deep learning, graphics apps |
| Google Cloud GPUs | NVIDIA Tesla GPUs attached to Compute Engine VMs | Video transcoding, GIS, finance |
| IBM Power System GPU Servers | Bare metal & virtualized GPUs for AI and analytics | Data science, ML Ops |
| Paperspace Gradient | GPU clusters purpose-built for machine & deep learning | Computer vision, NLP, recommendations |
| Vast.ai | P2P marketplace to access consumer, pro & datacenter GPUs | Graphics, gaming, compute |
| Lambda GPU Cloud | Virtual machines & infrastructure optimized for deep learning | Neural net training, model creation |
| Nimbix Cloud GPU Platform | Bare-metal GPU workstations and HPC infrastructure | Engineering simulations, rendering |
| OVHcloud GPU Instances | Bare-metal GPU servers powered by NVIDIA Tesla V100 | Machine learning, AI development |
| Exxact Cloud GPU Solutions | Tailored HPC infrastructure with latest GPU tech | Manufacturing, finance, EDA |
| Qarnot GPU.server | Environmentally friendly edge computing/rendering | Animation, VFX production |
Let‘s analyze the leaders in cloud graphics processing – AWS, Microsoft Azure and Google Cloud.
AWS Cloud GPU Options
The cloud colossus Amazon Web Services supports a multitude of NVIDIA GPUs across their EC2 computing instances:
- Tesla T4 for machine learning inference
- Previous gen Tesla M60 GPUs
- Tesla P4 and V100 for analytics/HPC
- Quadro virtual workstations for graphics
These can be clustered for scale-out performance. AWS also provides pre-optimized AI container images via the NVIDIA GPU Cloud for quick deployment.
Microsoft Azure GPU Virtual Machines
At Microsoft Azure, GPU capabilities come through their N-Series VMs specifically designed for intensive graphics and compute:
- NVv4 – AMD Radeon Instinct MI25 GPUs
- NC T4 v3 – NVIDIA Tesla T4 Tensor Core
- NDv2, NCv3 – NVIDIA Tesla V100 NVLink
Like AWS, Azure offers a breadth of NVIDIA GPU options married to a full-featured public cloud environment. These provide excellent acceleration for Azure-native tools like Machine Learning Service.
Google Cloud TPUs and GPUs
Google Cloud Platform features advanced hardware under its Compute Engine banner:
- NVIDIA Tesla T4 – focused on AI inference
- NVIDIA Tesla P4 – cost-effective ML training
- NVIDIA Tesla V100 – highest performance for HPC & graphics
- NVIDIA Tesla A100 – Ampere-based GPU for data analytics
In addition to these familiar NVIDIA models, Google Cloud stands out by introducing their custom Tensor Processing Units (TPUs). These ASIC chips specifically target deep learning workloads. TPUs attached to VMs provide massive lifting power for TensorFlow models and other neural networks.
The major cloud providers demonstrate the pivotal role NVIDIA has played in accelerating key workloads through CUDA and their popular software ecosystem. Now let‘s examine alternatives.
Specialized Cloud GPU Providers
While the hyperscalers feature well-rounded public cloud environment with integrated GPU resources, smaller players focus specifically on high performance compute:
Paperspace – ML Ops in the Cloud
Paperspace Gradient caters to the exploding domain of MLOps – productive lifecycle management for enterprise machine learning. Their GPU clusters comes preloaded data science packages like Jupyter and PyTorch to fast-track AI project development.
Some other nice touches are one-click notebooks, open-source model templates and support for version control with Git. Together this simplifies collaboration across data science teams.
Vast.ai – Decentralized Supercomputing
Vast.ai connects individuals needing GPU horsepower with an organic network of compute providers. Their decentralized, peer-to-peer approach pools together consumer and datacenter hardware to form an elastic GPU grid. Unique benefits include:
- Access rare or niche GPU makes/models on-demand
- Community support model enhances engagement
- Lower costs through sharing model rather than middlemen
- Excellent flexibility choosing exact GPU config through automated auction marketplace
For researchers and startups, vast.ai opens up more experiments at lower Infrastructure-as-a-Service rates.
Lambda GPU Cloud – Purpose Built for Deep Learning
As the name suggests, Lambda GPU Cloud focuses like a laser one key application – deep neural network model building. Their VMs come pre-loaded with all the latest frameworks like TensorFlow and PyTorch. Lambda GPU Cloud takes care of the rest providing:
- High speed networking up to 10 Gbps inter-node bandwidth
- Optimized drivers, libraries and computing environments
- Juypyter Notebook support lowers barrier to entry
- Scales to 100+ GPUs for distributed training
- Starts at just $1.25 per hour
For data scientists and ML engineers, Lambda GPU Cloud delivers exceptional convenience coupled with bleeding-edge infrastructure.
Nimbix Cloud GPU Workstations
Texas-based Nimbix offers purpose-built workstations and servers for intensive compute in the areas like:
- Oil & gas – seismic imaging
- Automotive – aerodynamics simulation
- Media – 3D rendering and effects
Their Nimbix Cloud GPU Platform provides instant access to Windows and Linux environments tailored for CAE, CFD and other engineering applications. With bare-metal performance plus license pooling, Nimbix allows engineers to maximize productivity.
This sample illustrates the range of domain-specific cloud GPU solutions now available alongside general purpose options from AWS et al. Determining the best platform depends on your specific use case – from AI inferencing to video effects rendering.
Now let‘s discuss key considerations when evaluating cloud GPU providers.
Choosing the Best Cloud GPU Solution
With the wealth of GPU cloud solutions now available, selecting the right platform to meet your technical and business needs requires careful inspection across a range of criteria:
GPU Types and Generations
The specific NVIDIA GPU model (Tesla T4, Quadro RTX 6000, etc.) determines performance based on:
- Processing cores – Tensor TPUs vs CUDA
- Memory – GBs of video RAM
- Compute capability – teraflops & clock speeds
- AI optimizations – tensor & ray tracing cores
Newer generations like Ampere deliver better deep learning support and efficiency. Testing options using your models and data is prudent.
Supporting Hardware
Besides the GPUs themselves, available vCPU cores, RAM capacity, storage types (HDD vs SSD vs NVMe) and interconnect fabric impact real-world speed. Balancing these components to avoid bottlenecks is non-trivial.
Software Environment & Frameworks
Optimized machine learning frameworks (PyTorch, TensorFlow, Caffe, etc.), drivers, libraries and OS supported can accelerate work considerably over vanilla environments.
Networking Capacity
Ensuring low latency and high bandwidth networking is crucial for large dataset transfers or models training in parallel. Verify interconnect bandwidth and topology.
Security & Compliance
Transmitting proprietary intellectual property or personal data to the cloud demands rigorous controls around:
- Encryption technologies utilized
- Physical data center protections
- Access management policies and practices
- Internationally recognized compliance standards adherence
Support & SLAs
Despite abstraction the cloud provides, hardware issues can arise so professional support and uptime guarantees give peace of mind for mission-critical workloads.
Billing model
Weigh fixed monthly pricing against pure pay-as-you go models.Blending may prove optimal aligning with project timelines.
Hybrid & Multi-Cloud
Rather than cloud GPU access being an all-or-nothing decision, hybrid provides flexibility. Connecting on-prem GPU resources with public cloud capacity delivers an enterprise-grade solution.
Evaluating combinations of these variables in the context of your specific applications and planned usage nets the best fit.
Real Business Benefits of Cloud GPUs
Let‘s spotlight a few examples of organizations unlocking innovation using on-demand graphics acceleration:
Accelerating Drug Discovery with Cloud Biosciences
UK-based Cloud BioSciences provides a platform for researchers to perform computer simulations modeling small molecule interactions with proteins. Their cloud HPC infrastructure featuring NVIDIA GPUs:
- Shortens client project timelines from months to weeks
- Allows investigation of 100x more compounds
- Cuts computing costs compared to traditional clusters
By leveraging cloud GPU services paired with expert support, Cloud BioSciences makes once cost-prohibitive biomolecular modeling accessible.
Edtech Supports 60x More 3D Animation Students
The online education provider Animate 3D taps cloud GPU leader Paperspace to offer an affordable platform for its aspiring animators and modelers. By offloading rendering of rich graphics to the cloud, Animate 3D can cost-effectively support rendering upto 60x more student projects compared to local hardware.
Financial Services Firm Improves Real-time Risk Models
A leading capital markets enterprise uses dedicated servers with high-end NVIDIA Quadro GV100 graphics accelerators via Qarnot Computing. Their quantitative analysts and data engineers can iterate faster improving accuracy of machine learning algorithms for trade analytics and fraud detection.
Leveraging cloud GPUs has slashed development cycles from months to days while keeping proprietary IP secure.
These examples illustrate how organizations of any size can innovate faster and for less cost using modern cloud GPU solutions.
Cloud GPUs – Looking Ahead
As artificial intelligence, parallel computing and immersive media transform industries, expect relentless innovation from GPU vendors and cloud platform partners. Some trends to keep an eye on include:
Cloud AI Marketplaces & GPU-as-a-Service
Managed solutions like IBM Maximo Visual Inspection encapsulate the complexity of directly provisioning cloud infrastructure and machine learning toolkits. Industry-specific AI building blocks speed adoption by non-experts. Extending as-a-Service models to GPU/TPU hardware lowers barriers further still.
Virtual GPU Pooling
Splitting up physical GPUs into smaller virtual instances for sharing allows more fine-grained allocation aligned to workload needs and budgets. Think timeshare model instead of dedicated use.
Confidential Computing
Encrypting data from the moment it leaves the host system using GPU-powered secure enclaves boosts protection for patented IP and private data moved cloud-side. Confidential computing safeguards competitiveness.
Liquid Cooling & Sustainable Data Centers
Faster, greener infrastructure like NGD‘s Computational Fluid Dynamic data centers reduce power demands for GPUs. Direct contact liquid cooling plus renewable energy sources curb environmental impact even as cloud GPU adoption swells.
Conclusion – The Sky‘s the Limit with Cloud GPUs
Graphics acceleration used to require investing hundreds of thousands in on-premise hardware costing substantial time, money and manpower to deploy and maintain. Today through the magic of hyperscale cloud platforms, specialized providers and virtualization, engineers, researchers, designers and developers can simply dial up phenomenal GPU-powered computing capacity on demand for pennies per hour.
This game changing capability unlocks once unattainable ideas – be it real-time life-saving medical insights via AI imaging analysis or immersive virtual worlds that push imagination‘s boundaries. Cloud GPUs tear down the barriers of cost, complexity and scarce talent that hindered such cutting edge innovation.
So whether you need lightning fast deep learning model iteration or pixie-dust visual effects rendering fueling creativity, cloud GPU solutions have you covered. We‘ve only scratched the surface of the business benefits and technological wonder they unleash. What will you build?
