How to choose a cloud GPU for your AI/ML projects

The need for physical servers has decreased in favor of cloud service providers lately. But what about those in need of external computing power to run AI/ML project simulations and data analysis? Thankfully, cloud service providers like Liquid Web are there to jump in and come to the rescue.

In particular, their dedicated GPU hosting offerings caught my eye, since they provide single-tenant servers built specifically for high-performance workloads. Instead of fractional or virtualized GPUs, you get full, uninterrupted access to the hardware. This means maximum speed, consistent performance, and none of the security or resource-sharing risks that come with cloud-based or shared GPU solutions.

Together with my team of experts here at Cybernews, I explored GPU hosting solutions at Liquid Web, and I’ll share my findings on them, as well as give you a step-by-step guide on choosing the best option for your needs below.

AI 101

The global AI market has reached staggering heights, and it’s now considered to be worth nearly $200 billion. However, the biggest spike is yet to be expected, and as some experts suggest, the total market value could go well over $1.8 trillion by 2030.

Now, I believe you’ll agree when I say that’s a lot of zeroes, but we’re just barely scratching the surface here. My point is that, with such massive market growth, the need for flexible and scalable solutions for running AI infrastructures can only go up.

Artificial intelligence and machine learning projects require a ton of computing power, and some enterprise-grade GPUs like the NVIDIA H100 can reach mind-blowing prices of over $30,000.

While that might be something large enterprises can afford, small businesses and startups most certainly can’t. The solution is simple: instead of purchasing a high-end card, you can simply rent it.

What's the difference between cloud GPU and dedicated GPU hosting?

Cloud GPUs and dedicated GPU servers may look similar at first glance, but they work very differently. A cloud GPU is a fractional service: a provider uses virtualization to split a physical GPU into multiple smaller units, rented out to different customers.

This makes cloud GPUs more affordable and flexible, since you can scale resources up or down and pay only for what you use. However, performance can be inconsistent, and you’re still sharing hardware with other tenants.

By contrast, a dedicated GPU server (also called bare metal GPU hosting) gives you the entire physical machine. There’s no virtualization layer, no resource sharing, and no performance overhead.

You get direct access to all of the server’s CPU, RAM, storage, and GPU power – making it the go-to choice for long-term, performance-critical workloads such as AI training, medical imaging, cybersecurity, or scientific computing.

Benefits of dedicated GPU server hosting vs cloud GPU options

Before I expand a bit on the main quirks and features of LiquidWeb cloud-based GPUs, let’s take a moment to consider the key benefits of using one:

• Improved scalability. One of the main benefits of cloud-based GPUs is their scalability. You can start training new models and developing AI/ML solutions from scratch while only using a fraction of the available power. Then, once your project takes flight, simply increase the power demand and the pricing model accordingly. With services like LiquidWeb, you can do this time and time again according to your needs.
• Chance to rent the latest GPUs. As mentioned, buying a high-end enterprise GPU like the NVIDIA H100 is extremely costly. Even so, cloud services like LiquidWeb allow you to take your piece of the pie and use the computing power of the H100 and similar chips without having to purchase them yourself. Plus, whenever a new GPU is out with a power frame that’s more suitable for your project, you can just switch to it.
• Affordability. This one needs no explanation since the math is simple. You can save a lot of money to invest in the development of your company by simply renting a GPU instead of buying one.
• No maintenance costs. Last but not least, you can save up on maintenance costs by using cloud computing services like LiquidWeb. Your only obligation is to pay your monthly subscription, and the provider will take care of all the maintenance to ensure that your chip is running smoothly internally.

How to choose the right GPU server hosting provider

Now that you know a bit more about GPU hosting services overall, let’s take some time to show how you can choose the right server and hosting provider for your needs. I’ve done some research based on the GPUs provided by Liquid Web, and here’s the score:

• Consider your AI training workload. The first thing you should consider is the AI training workload and how much power you need to run the infrastructure. You should check the computer vision model development and LLM requirements before you pick a GPU. In most cases, for proper training, you need lots of computing power and video random access memory, so you can make your final pick accordingly.
• Check the graphics and rendering capacity. Another key feature to look out for is the graphics and rendering capacity. For this sake, as well as for its overall video processing specs, Liquid Web offers the NVIDIA L4 ADA chip.
• Consider the performance specs overall. If you need maximum computing power and performance, you can choose GPUs like the NVIDIA L40S, Liquid Web’s replacement for the L40, which had a lower power output.
• Look for multiple GPU systems. For large-scale projects, you can consider Liquid Web’s multi-GPU systems. In other words, you can combine the computing power and specs of several different systems and get the best package for your project. Make sure to consider NVLink support for inter-GPU communication, as well as conduct proper research on how well you can share the project data across multiple GPUs.

Best GPUs to go with

To show you just a glimpse of what you could get with a GPU, I dug into different NVIDIA chips. That’s how I got to the best few options to go with for different types of AI/ML projects, and here’s the breakdown:

• NVIDIA L4 ADA. The L4 ADA is a bit dated now, but it’s still a great GPU for running AI inference and training models. It’s also among the most affordable options. I’d call it the best overall option for running AI-based data processing.
• NVIDIA L40S. The L40S ADA is a successor to the L4 GPU, and it’s one of NVIDIA’s best price and quality blends. It’s an excellent pick for generative AI models and LLM, so I’d recommend it to organizations in need of a tailored solution for a wide variety of AI tasks.
• NVIDIA H100 NVL. Finally, the 94GB H100 would be my top choice for a chip for AI training, predictive analysis, and large-scale machine learning and computing. I’d recommend it to those looking for an enterprise-grade solution, which is not surprising considering the price tag you’d have to be willing to pay to purchase this chip.

What Liquid Web brings to the table with its dedicated GPU servers

If you’re just getting started in the AI/ML realm and looking for the best GPU hosting, Liquid Web ticks all the boxes. I’ve thoroughly checked their offer, and I have to say I was pleasantly surprised by how advanced some of the stacks are.

The provider offers a complete pre-configured GPU stack perfectly tailored for AI/ML GPU workloads. It starts with a comprehensive framework for each NVIDIA GPU, which is known to set the industry standard in this category.

Some of their options, such as the L40S and the H100 NVL, are well-rounded for neural network training and AI development as a whole. Along with top-grade chips, Liquid Web also offers stack framework support through TensorFlow and PyTorch. These make your AI or ML integration smoother without the need to optimize other system components to suit the new and powerful GPU.

Plus, you can get access to a wide range of pre-trained models and optimized frameworks through NVIDIA integration when you rent a GPU through Liquid Web.

Core components that you get with Liquid Web

Along with computing power, the system includes other core components essential for AI/ML development. Here’s an overview of what you’ll get:

• Compatible drivers and toolkits. Choosing Liquid Web’s NVIDIA chips also guarantees compatible driver installation and dedicated toolkits like the CUDA toolkit as a complete environment for high-performance and GPU-accelerated applications.
• GPU-accelerated libraries. Once you sign up for Liquid Web’s GPU server hosting, you’ll immediately get adequate tools. For instance, I’ve seen the brand promoting its NVIDIA chips with the CUDA toolkit, which is basically a GPU-accelerated library for deep neural networks. Plus, it speeds up AI model training and inference.
• Fast and easy deployment. Thanks to the NVIDIA Container toolkit, which you’ll get together with the core components, you can easily deploy GPU-accelerated containers.
• Framework support. Finally, Liquid Web’s GPU stack is pre-configured, which means you’ll pretty much get all the support frameworks you could need, including Google’s TensorFlow.

Final take

Ultimately, the exact type of GPU hosting that you’ll choose depends entirely on your needs and project type. Still, one thing is for sure: it’s a way more affordable solution compared to purchasing an on-premesis GPU server.

Overall, I’d recommend going with the L4 ADA for cost-effective and more “basic” AI projects, if there is such a thing. For a higher-output option, you can go with the newer L40S, or go full-scale and get an enterprise-level solution like the H100 NVL.

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