How to choose a GPU Cloud provider for AI projects: key criteria

Why choosing a GPU Cloud provider becomes critical for AI

AI projects require significantly more computing resources than traditional applications.

Training machine models, working with large language models (LLM), generative AI, and image processing require:

• high GPU performance;
• fast data access;
• scalable infrastructure;
• stable operation of computing resources.

At the same time, the choice of a GPU Cloud provider affects not only the speed of computations but also data security, the cost of operation, and the ability to scale AI projects.

Therefore, when choosing a platform, it is important to evaluate not only the price of renting a GPU but also the entire technology stack.

1. NVIDIA certification and support for the AI ecosystem

The first criterion when choosing a GPU Cloud provider is the availability of certified NVIDIA infrastructure.

NVIDIA is developing its own ecosystem for enterprise AI, which includes:

• GPU accelerators;
• NVIDIA AI Enterprise software;
• tools for developing and deploying AI models;
• certified server platforms.

Why is this important?

Compatibility with the NVIDIA ecosystem helps companies:

• launch AI workloads faster;
• use optimized libraries;
• reduce the risks of hardware and software incompatibility.

For example, NVIDIA AI Enterprise includes a set of software tools for developing and deploying production AI applications, including support for popular AI frameworks.

When choosing a GPU Cloud provider, it’s worth checking:

✅ which GPUs are available;

✅ whether CUDA and NVIDIA drivers are supported;

✅ whether there are certified server platforms;

✅ whether NVIDIA AI Enterprise is supported.

2. Local data placement (Data Residency)

For many companies, the issue is not only about GPU power, but also about where the data is physically located.

AI projects often work with sensitive information:

• internal corporate data;
• customer information;
• financial documents;
• medical or industrial data.

Therefore, companies pay attention to:

• the location of data centers;
• the requirements of local legislation;
• data storage policies.

Why is local infrastructure important?

Placing GPU infrastructure in the region where the business operates can help:

• reduce latency;
• simplify compliance requirements;
• provide control over the data storage location.

For example, for companies in GCC countries, the availability of infrastructure within the region may be an important factor.

When choosing a provider, it is worth clarifying:

✅ Where are the data centers located?

✅ Where is the AI model data stored?

✅ Is there a possibility to choose the region of placement?

3. SLA and infrastructure reliability

AI projects may depend on the constant availability of computing resources.

A downtime of the GPU infrastructure can lead to:

• stopping model training;
• delaying product launch;
• losing computing time.

Therefore, it is important to evaluate the SLA (Service Level Agreement).

What to check in an SLA:

1. the guaranteed level of availability;
2. the response time of technical support;
3. the service recovery conditions;
4. the provider’s responsibility.

Major cloud providers publish SLAs for their services as a commitment regarding infrastructure availability.

For example, the AWS EC2 SLA defines service availability guarantees for virtual servers.

4. Kubernetes support for managing AI workloads

Modern AI projects are increasingly using Kubernetes as a platform for managing containerized applications.

Kubernetes allows you to:

• automate application deployment;
• manage containers;
• scale workloads;
• distribute computing resources.

For GPU infrastructure, Kubernetes uses NVIDIA GPU Operator, which automates the configuration of GPU resources in Kubernetes clusters.

Why is Kubernetes important for AI?

For example, a team can use Kubernetes to:

• launch multiple AI models;
• manage inference services;
• allocate GPUs between projects;
• automatically scale.

When choosing a provider, it’s worth checking:

✅ whether there is a Kubernetes Managed Service;

✅ whether NVIDIA GPU Operator is supported;

✅ whether ML workloads can be run via containers.

5. GPU Types: Choose Based on the Workload, Not Just Performance

There is no single GPU that is suitable for every AI project.

Different workloads require different specifications:

When choosing a provider, consider:

✅ Which GPU models are available

✅ The amount of GPU memory

✅ Support for the latest NVIDIA GPU generations

For example, the NVIDIA H100 is designed to accelerate AI and HPC workloads, while the newer NVIDIA Blackwell generation is optimized for generative AI and large-scale AI infrastructure.

6. Network: data transfer speed affects AI. 

GPU is only part of the AI infrastructure.

For effective model operation, the following are important:

• data transfer speed;
• network latency;
• bandwidth between GPUs;
• storage access speed.

This is especially important for:

• distributed model training;
• working with large datasets;
• clusters consisting of multiple GPUs.

When choosing a provider, you should pay attention to:

✅ which network is being used;

✅ Are there high‑speed connections between GPUs?

✅ What network technologies are available?

7. Storage: infrastructure for large AI data

GPU is only a part of the AI infrastructure.

For effective model operation, the following are important:

• data transfer speed;
• network latency;
• bandwidth between GPUs;
• storage access speed.

This is especially important for:

• distributed model training;
• working with large datasets;
• clusters consisting of multiple GPUs.

When choosing a provider, you should pay attention to:

✅ what network is used;

✅ whether there are high‑speed connections between GPUs;

✅ What network technologies are available.

8. GPU Cloud Provider Selection Checklist

Before choosing a provider, evaluate the following:

ITGLOBAL.COM GPU Cloud for AI Workloads

ITGLOBAL.COM provides scalable GPU Cloud infrastructure for AI, machine learning, LLM inference, computer vision, generative AI, and high-performance computing workloads. The platform offers access to modern NVIDIA GPUs, including NVIDIA H200 and RTX PRO 6000 Blackwell Server Edition, without the need to purchase and maintain physical servers. Customers can scale computing resources as their projects grow and combine GPU capacity with cloud storage, networking, and other infrastructure services. The infrastructure is hosted in Tier III data centers and supported by ITGLOBAL.COM engineers 24/7, helping companies deploy and operate AI workloads reliably.

Conclusion

Choosing a GPU Cloud provider is not just about the price per GPU hour.

For a successful AI project, it is necessary to evaluate the entire infrastructure:

• which GPUs are available;
• how compatible the platform is with the NVIDIA AI ecosystem;
• where the data is located;
• how reliable the service is;
• whether the provider supports Kubernetes;
• what network and storage are used.

A properly chosen GPU Cloud platform allows companies to launch AI projects faster, scale computing resources, and use modern technologies without having to build their own complex infrastructure.