◦ Optimized configs
◦ Industry-leading support
GPU → GPU as a Service
What is GPU as a Service (GPUaaS)?
GPU chips and servers are transforming dozens of industries at an exciting pace. Almost every data-driven business needs access to GPU power to keep up, and GPU server hosts are doing good, quick work to make these powerful machines available to a wide range of users.
One of the latest is GPU as a Service (GPUaaS). It’s similar to cloud GPU … but not exactly. Let’s clarify what exactly GPUaaS means, the benefits it offers, and how it differs from other GPU server options, so you have what you need to choose the right option.
Get premium GPU server hosting
Unlock unparalleled performance with leading-edge GPU hosting services.
What is a GPUaaS (GPU as a service)?
GPU as a Service (GPUaaS) is a cloud-based offering that provides access to powerful graphics processing units (GPUs) on-demand. Instead of purchasing and maintaining GPU hardware, businesses and individuals can rent GPU resources through a cloud provider, scaling their computing power as needed.
GPUaaS virtualizes high-performance GPUs and makes them available over the internet, similar to traditional cloud computing services. Hosting providers allocate GPU instances based on user requirements, allowing for flexible pricing models, including pay-as-you-go or subscription plans.
This service is commonly used by developers, researchers, and content creators who need high-performance computing without investing in dedicated hardware.
GPUaaS benefits
GPU as a service provides several benefits that make it an attractive solution for businesses, developers, and researchers who want high-performance computing.
- Cost efficiency: Instead of investing in expensive GPU hardware, users can access GPUs on-demand. Pay-as-you-go pricing models also help optimize costs based on usage.
- Scalability: GPUaaS lets users scale resources up or down based on workload demands.
- Environmentally savvy: GPUaaS leverages existing, unused processing units, saving on energy consumption.
- Access to high-performance GPUs: Cloud providers offer cutting-edge GPU technology, ensuring users always have access to the latest hardware without upgrading their own infrastructure.
- Flexibility: Users can deploy GPU resources from anywhere with an internet connection, enabling remote access and seamless collaboration across teams.
- Reduced maintenance and management: Since the cloud provider handles hardware maintenance, updates, and security, users can focus on their projects instead of worrying about system upkeep.
GPUaaS vs on-premise GPU
GPU as a Service and on-premise GPU servers each have distinct advantages and trade-offs, depending on the specific needs of a business or individual. Here’s a comparison of the two:
Cost and investment
- GPUaaS: Eliminates upfront hardware costs, offering a pay-as-you-go or subscription-based pricing model.
- On-premise GPU server: Requires a significant initial investment in hardware, along with ongoing costs for power, cooling, and maintenance. However, it can be more cost-effective over time for workloads that require continuous high-performance computing.
Scalability
- GPUaaS: Provides easy scalability, allowing users to increase or decrease GPU resources as needed.
- On-premise GPU server: Limited by physical hardware capacity. Scaling requires purchasing and installing additional GPUs, which can be time-consuming and costly.
Performance and latency
- GPUaaS: While cloud providers offer powerful GPUs, network latency can impact performance for real-time applications, such as gaming or high-frequency trading.
- On-premise GPU server: Offers low-latency performance since processing happens locally without reliance on an internet connection.
Maintenance and management
- GPUaaS: Cloud providers handle all maintenance, software updates, and hardware upgrades, reducing the need for in-house IT management.
- On-premise GPU server: Requires dedicated IT staff to manage, update, and troubleshoot hardware and software issues.
Security and compliance
- GPUaaS: Security depends on the cloud provider, and data is stored off-premises, which may raise concerns for industries with strict compliance regulations.
- On-premise GPU server: Provides full control over data security and compliance.
GPUaaS vs cloud GPU
GPUaaS and cloud GPUs are closely related, but there are some key differences in how they are offered and managed.
Definition and scope
- GPUaaS: A fully managed service that provides users with access to GPU computing power via a cloud provider. It often includes additional features such as software integrations, APIs, and automation to simplify GPU usage for AI, machine learning, and rendering applications.
- Cloud GPU: A raw GPU instance available in the cloud, allowing users to configure and manage their own GPU environment. It provides direct access to virtualized GPUs but requires more setup and management compared to GPUaaS.
Ease of use
- GPUaaS: Designed for simplicity and accessibility, often with pre-configured environments for AI, deep learning, and analytics. Users don’t need extensive infrastructure knowledge to get started.
- Cloud GPU: More flexible but requires users to configure and manage their own GPU instances, including installing necessary software, drivers, and libraries.
Management and maintenance
- GPUaaS: Fully managed by the provider, including updates, optimizations, and software support. Ideal for businesses or developers who want to focus on workloads without worrying about infrastructure.
- Cloud GPU: Users must handle setup, maintenance, and software updates themselves, making it a better choice for those who need more control over configurations.
Scalability and customization
- GPUaaS: Typically offers a more structured approach, with pre-configured plans optimized for specific workloads (e.g., AI training, video rendering). Scalability is straightforward but may be limited by the provider’s service tiers.
- Cloud GPU: Provides greater flexibility in choosing GPU types, memory, and configurations. Users can customize the environment to fit their specific needs, but scaling may require more manual intervention.
Cost considerations
- GPUaaS: Often comes with a subscription-based or pay-per-use model, with pricing that includes management and support services.
- Cloud GPU: Users pay for raw GPU resources, which can be cheaper for those with technical expertise who don’t need additional management services.
GPUaaS is a more user-friendly, managed service, making it ideal for those who want to leverage GPU power without handling infrastructure details.
Cloud GPU, on the other hand, provides more flexibility and customization but requires users to manage their own environment. The choice depends on whether ease of use or control is the priority.
GPUaaS vs bare metal GPU
GPU as a Service (GPUaaS) and bare metal GPU server hosting both provide access to high-performance GPUs, but they differ significantly in control, performance, scalability, and cost structure.
Performance and latency
- GPUaaS: Offers virtualized GPU resources shared among multiple users. While modern cloud providers optimize performance, virtualization overhead can introduce minor latency. GPUaaS is ideal for workloads that require flexibility but not necessarily the absolute lowest latency.
- Bare metal GPU hosting: Provides dedicated access to a physical GPU server, eliminating virtualization overhead and ensuring maximum performance. This is critical for applications like real-time AI inference, financial modeling, and high-performance gaming.
Control and customization
- GPUaaS: Offers limited customization since users work within the cloud provider’s environment. It is designed for ease of use but may restrict certain configurations or software setups.
- Bare metal GPU hosting: Provides full control over the hardware, OS, and software stack. Users can fine-tune settings, install custom drivers, and optimize performance for specific workloads.
Scalability
- GPUaaS: Highly scalable, allowing users to provision and release GPU instances on demand. Ideal for businesses that experience fluctuating workloads, such as AI training or batch processing.
- Bare metal GPU hosting: Less dynamic in terms of scaling. Deploying additional servers requires manual provisioning and can take time, making it better suited for sustained workloads.
Cost and pricing model
- GPUaaS: Typically follows a pay-as-you-go or subscription-based model, making it cost-efficient for short-term or intermittent workloads. No upfront investment is required.
- Bare metal GPU hosting: Involves a fixed monthly or yearly cost for dedicated hardware, which can be more cost-effective for workloads requiring continuous GPU usage.
Maintenance and management
- GPUaaS: Fully managed by the provider, meaning users don’t have to worry about hardware failures, updates, or maintenance.
- Bare metal GPU hosting: Requires more management, as users are responsible for OS updates, security patches, and potential hardware replacements. Some hosting providers offer managed services for an additional fee.
Security and compliance
- GPUaaS: Data is stored in the cloud, which may not be ideal for organizations with strict security or compliance requirements.
- Bare Metal GPU Server Hosting: Offers greater control over data security, making it preferred for industries that require on-premises-level security, such as finance, healthcare, and government agencies.
Choose GPUaaS if you need flexibility, scalability, and lower upfront costs with a managed service.
Choose bare metal GPU hosting if you require dedicated performance, full control, and long-term cost efficiency for sustained workloads.
Use cases
- GPU as a service (GPUaaS): Ideal for startups, researchers, and businesses that need flexible, scalable computing power without large upfront investments. It’s well-suited for AI training, rendering, and cloud-based applications.
- Cloud GPU: Suitable for users who need full control over their GPU environment but still aren’t ready for a full server, such as enterprises with specific configurations or those running customized machine learning pipelines.
- Bare metal GPU server hosting: Best for businesses that require consistent, high-performance computing with full control over the environment, such as AI inference, real-time processing, and long-term GPU-intensive workloads.
- On-premise GPU server: Best for organizations that require persistent, high-performance computing with minimal latency and full control over security and compliance.
How to choose a GPU hosting provider (8 key considerations)
When selecting any type of GPU hosting provider, it’s essential to consider several key factors.
One of the most critical is performance and GPU specifications. The provider should offer high-performance GPUs. Check the memory and VRAM available on the GPU, as applications such as deep learning, AI, and 3D rendering require significant VRAM capacity.
Evaluate pricing models and cost efficiency carefully. Some providers charge hourly or per-minute rates, while others offer monthly or annual pricing. Choosing the right model depends on your budget and how long you plan to use the service.
And be aware of hidden fees, such as data transfer costs, storage fees, or additional charges beyond GPU usage.
Another important consideration is scalability and flexibility. If your workload varies, you’ll need a provider that allows on-demand scaling so you can increase or decrease GPU resources as required. If your application requires multiple GPUs, ensure the provider supports multi-GPU configurations with high-speed interconnects.
You should also consider data transfer and storage options. Bandwidth and latency can impact performance, especially when handling large datasets. Some providers offer high-speed cloud storage or NVMe SSDs, which significantly improve data access speeds.
Reliability and uptime are crucial, particularly for business-critical applications. A strong service level agreement (SLA) that guarantees 99.99% uptime or higher ensures your resources remain available. Additionally, data redundancy and automatic backup features can protect your work against unexpected failures.
The level of management and support offered varies between providers. Some offer fully managed services that handle updates, monitoring, and maintenance, while others require users to manage their own environment. If you need assistance, 24/7 customer support with GPU-specific expertise is beneficial, particularly if you run mission-critical workloads.
Server security is another key consideration. The provider should comply with data privacy regulations and industry standards such as GDPR, HIPAA, or SOC 2—especially if you’re handling sensitive data. Security features like firewalls, encryption, and DDoS protection should be in place to safeguard your workload from cyber threats.
Finally, provider reputation and ecosystem can make a big difference in your overall GPU hosting experience. Checking user reviews, testimonials, and case studies can provide insights into the provider’s reliability.
Getting started with GPU servers
There are varying degrees of GPU access, so your first step is to weigh your options against your needs and figure out which GPU model is right for you. Consider that you need GPU power for now, but also what your requirements might reasonably be 12 months from now.
When you’re ready for bare metal GPU server hosting, Liquid Web has you covered. Our NVIDIA GPU servers are optimized for peak performance, meet every compliance standard, are pre-configured for popular AI/ML frameworks, and more.
Click below to explore GPU server hosting options or start a chat right now to get specific answers and guidance.
Additional resources
What is a GPU? →
What is, how it works, common use cases, and more
What is GPU memory? →
Why is it important? How much do you need? And more …
Cloud GPU vs GPU bare metal →
Core differences, how to choose, and more
Brooke Oates is a Product Manager at Liquid Web, specializing in Cloud VPS and Cloud Metal, with a successful history of IT/hosting and leadership experience. When she’s not perfecting servers, Brooke enjoys gaming and spending time with her kids.