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On demand: Intro to GPU hosting with NVIDIA processors

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Explore how high-performance GPU solutions revolutionize AI, machine learning, content creation, and data analytics. Learn the fundamentals and find out how Liquid Web’s GPU hosting can power your next project. Watch now for expert insights.


As businesses and developers push the boundaries of high-performance computing, GPU hosting has become essential for AI, machine learning, data analytics, and rendering applications.

In this on-demand webinar, Liquid Web will introduce GPU hosting with NVIDIA processors, showcasing how businesses can leverage powerful GPU solutions for their workloads.

What you’ll learn

  • What GPU hosting is and why it matters in today’s data-driven landscape.
  • Key differences between GPU and CPU environments.
  • The advantages of NVIDIA GPUs in hosted infrastructure.
  • How Liquid Web’s solutions deliver power, scalability, and support.
  • Answers to common questions about deployment, costs, and getting started.

Whether you’re evaluating GPU hosting for the first time or looking to upgrade your infrastructure, this webinar will equip you with the knowledge and confidence to take the next step.

Watch now.

Webinar recap: Intro to GPU hosting with Liquid Web

Liquid Web hosted an insightful webinar titled “Intro to GPU Hosting,” where experts Brooke Oates and Chris La Nasa unpacked the growing importance of GPU hosting, its practical applications in AI/ML, and how organizations can leverage it to accelerate innovation.

The session attracted participants from across industries who are curious about how GPUs can improve performance, reduce time-to-insight, and enable the next generation of AI solutions.

Why GPU hosting matters for AI & ML

The session opened with a fundamental comparison between traditional CPU and GPU-accelerated servers. Brooke explained that while CPUs are great for general-purpose tasks, they fall short in parallel processing, which is essential for AI/ML workloads like model training and inference.

“GPUs are built for highly parallelized mathematical computations, especially those involving tensor cores, which are the core of modern machine learning,” she explained.

Unlike CPUs, which process a few threads quickly, GPUs can handle thousands of concurrent operations, making them indispensable for tasks like deep learning, high-resolution imaging, and complex data analytics.

The power of NVIDIA and the AI ecosystem

Brooke also emphasized why NVIDIA remains the leader in this space. Not only does NVIDIA dominate in hardware, but it also provides a robust ecosystem of software tools (like CUDA and cuDNN) that developers rely on to build AI-driven applications.

This broad ecosystem ensures that NVIDIA GPU processors integrate seamlessly with popular frameworks like TensorFlow, PyTorch, and others, maximizing compatibility and long-term value.

Real-world applications of AI across industries

Chris La Nasa stepped in to connect the dots between infrastructure and impact, outlining how AI is transforming various industries:

  • Retail & ecommerce: Personalized shopping experiences and predictive inventory management.
  • Sales & marketing: Enhanced customer segmentation and sales forecasting.
  • Customer support: Sentiment analysis, AI-powered chatbots, and knowledge delivery.
  • Cybersecurity: Real-time threat detection through anomaly analysis.
  • Healthcare: Faster and more accurate diagnostics through medical imaging.
  • Software development: AI models powering SaaS solutions and developer tools.
  • Education & research: High-performance computing for large-scale data analysis.

Chris also highlighted that AI investment is rapidly growing. One-third of companies plan to increase AI spending by 50 percent or more, yet many still lack performance testing or ROI analysis frameworks.

“AI is more than just chatbots. It’s reshaping business intelligence, customer experience, and operations,” he noted.

Getting started with GPU hosting

Brooke returned to share actionable insights on how to get started, focusing on self-hosted LLMs (large language models) versus pre-hosted AI services. Self-hosting offers:

  • Greater data privacy
  • More control over customization
  • Better alignment with unique business needs

However, it comes with financial and developmental investments, including the need for skilled teams and infrastructure planning. She suggested starting with tools like LLaMA to deploy base-level models and scale from there.

Liquid Web’s GPU solutions

To support businesses at every stage of their AI journey, Liquid Web provides purpose-built GPU servers.

Key features:

  • Architected for AI/ML performance (including PCIe throughput, NVMe drives, and CPU/GPU balance)
  • Includes pre-installed software stack: Ubuntu, CUDA toolkit, cuDNN, monitoring tools
  • Unified API with Terraform support for automated deployments
  • Options to scale from entry-level GPU systems to dual AMD EPYC + NVIDIA H100 servers

Brooke also discussed how Liquid Web’s broader hosting portfolio complements GPU systems:

  • Cloud VPS: Great for dev/testing, starting at $5/month
  • Bare metal cloud: Full hardware control with cloud scalability
  • Bare metal servers: Non-virtualized infrastructure for maximum performance

Why hosted GPU solutions make sense

In closing, the panel addressed a crucial question: How does hosted GPU infrastructure help control costs?

Brooke explained that the price of high-performance GPUs can exceed $40,000, and demand often creates supply chain bottlenecks. Hosted solutions offer:

  • Lower upfront costs
  • Immediate access to cutting-edge hardware
  • Elimination of operational burdens like cooling, power, and upgrades

Chris added that hosted GPU platforms also free teams from managing the physical infrastructure, ensuring focus stays on development and deployment.

Explore Liquid Web’s GPU hosting solutions

Whether you’re developing AI applications, training machine learning models, or running high-performance analytics, Liquid Web’s GPU hosting platform is built to deliver. Our infrastructure is purpose-engineered for intensive AI/ML workloads, offering NVIDIA-powered GPU servers, high-throughput NVMe storage, and enterprise-grade reliability.

With flexible deployment options, a unified API, and preconfigured software stacks, you can get up and running in minutes with performance that scales as you grow.Learn more and get started with GPU hosting today.

Explore Liquid Web’s GPU hosting solutions

Read the transcript

Please note that AI was used to remove filler words for clarity.

Kristina (Host):

[00:00:00]

SPEAKER: Kristina (Host)
Hello everyone, and welcome to today’s webinar: Intro to GPU Hosting, brought to you by Liquid Web. Thank you for joining us. We’re really excited to have you here. I’m Kristina, and I’ll be kicking things off before we dive into our session led by Brooke Oates and Chris La Nasa.

For those unfamiliar with us, Liquid Web is a premium hosting provider for businesses of all sizes. We offer reliable solutions like dedicated servers, cloud hosting, and GPU hosting backed by 24/7 support and over 20 years of experience. Today, we’ll cover:

  • What GPU servers are
  • Why you should choose NVIDIA
  • How to make GPU hosting work for you

This is more of an overview than a hands-on workshop, but we’ll show you what’s possible, what you’ll need, and how to get started. Brooke, can you kick us off by explaining what GPU-accelerated servers are and how they differ from traditional servers?

[00:02:06]

SPEAKER: Brooke Oates
Absolutely! Traditional systems typically include a CPU, memory, and disk space. That setup works well for many applications and hosting solutions.

However, AI and machine learning (ML) workloads are different. CPUs aren’t optimized for the level of parallel processing needed in AI/ML. These workloads require extremely fast and numerous calculations on tensors — advanced data structures used in AI training.

That’s where GPUs come in. They’re designed for high-volume, parallel computation. While CPUs have a limited number of cores, GPUs offer thousands, sometimes tens of thousands, of cores optimized for tensor calculations.

You can technically run AI/ML on a CPU using tools like TensorFlow or PyTorch, but performance will be significantly lower. CPUs are great for testing or development, but not ideal for production.

Now, it’s not just about dropping a GPU into a system. You need the whole system, CPU, memory, disk, bus architecture, designed around the GPU. For instance, NVMe drives are needed not just for storage, but for throughput. Systems must be built for these demands.

Why NVIDIA?

More than two-thirds of AI developers use NVIDIA hardware. Their GPUs, especially those built for data centers, are tailored for AI and ML. They also provide essential software tools like the CUDA toolkit and cuDNN, which are used in many off-the-shelf AI models.

So when you invest in NVIDIA, you’re getting top-tier hardware and ongoing software support, making it a solid long-term investment.

Chris, I think you have something to add?

[00:07:40]

SPEAKER: Chris La Nasa
Yes, and I’ll start with a question: We often think AI is just chatbots like ChatGPT. But if you’re not building a chatbot, why care about AI?

Well, AI isn’t just for chatbots. Companies use AI to understand customer behavior, analyze site performance, create content, detect hardware failures, and even predict customer churn.

Industry use cases

  • Retail & ecommerce: Personalized shopping experiences, predictive inventory management.
  • Sales & marketing: Improved customer insights and forecasting.
  • Customer service: AI-powered chat and sentiment analysis to assist agents.
  • Cybersecurity: Real-time threat detection through anomaly analysis.
  • Healthcare: Faster and more accurate diagnoses using high-res image analysis.
  • Education & research: AI-assisted big data analysis and high-performance computing.

We recently surveyed users about their GPU usage for AI workloads:

  • 33 percent of companies plan to increase AI spending by 50 percent or more.
  • Nearly two-thirds plan to increase spending by at least 20 percent.

However, less than 30 percent of these companies conduct performance testing or ROI analysis. Many are investing without a clear performance strategy.

This shows that AI is everywhere and growing fast, but companies still need guidance. That’s why partnering with experts, like those at Liquid Web, is so important.

SPEAKER: Kristina

[00:14:05]

Thanks, Chris. Now that we know what GPU hosting is and why it’s important, how should someone get started? What should they look for, Brooke?

Getting started with self-hosted LLMs

SPEAKER: Brooke Oates
Great question. The first step is deciding whether to use self-hosted or pre-hosted models like Gemini or ChatGPT. Self-hosted models require hardware and offer the biggest benefit: privacy.

Many companies restrict employees from inputting sensitive data into external AI tools. Self-hosting keeps your data in-house, eliminating those concerns. Hosted tools are great for quick integrations, but if you want deep AI/ML customization, self-hosting makes sense.

What you’ll need

  • Hardware: Including dedicated GPUs.
  • Development effort: Especially if integrating with internal systems.
  • Scaling considerations: Use fractional GPUs to start, then upgrade as needed.

Look for solutions specifically built for AI/ML, not just general-purpose servers. At Liquid Web, we offer architected GPU solutions tailored for AI and ML use cases.

Our systems include:

  • Dual AMD EPYC processors.
  • High-throughput NVMe drives (not just for space, but for speed).
  • Custom-designed architecture to avoid bottlenecks.

Liquid Web’s GPU stack

Our systems come with:

  • Pre-installed Ubuntu OS
  • CUDA toolkit, cuDNN
  • GPU monitoring tools like NVIDIA SMI and VTOP
  • AlarmNet integration

You can use our default OS or bring your own image, we’re flexible.

Beyond GPU hosting

AI hosting often requires more than just a GPU:

  • Cloud VPS for dev/testing
  • Bare metal cloud for single-tenant, high-performance infrastructure
  • Bare metal servers for non-virtualized deployments

All of this is accessible through our unified API — fully documented and Terraform-compatible for Infrastructure as Code workflows.

Q&A

[00:22:59]

SPEAKER: Kristina
Thank you, Brooke. That was a great overview. We’ve got just a few questions before we wrap up.

Q: How does GPU hosting improve performance over traditional CPU hosting for AI/ML workloads?
Brooke: CPUs can technically handle AI/ML, but GPUs are optimized for it. They perform massively parallel calculations that CPUs just can’t match. The performance difference is dramatic.

Q: GPU hardware is expensive. How does GPU hosting help businesses manage costs?
Brooke: Hardware prices have surged—some GPUs go for over $40,000. With hosting, you avoid up-front capital costs and unpredictable delivery times. Instead, you pay a monthly rate and always get current hardware.

Chris: Plus, hosting saves you from managing HVAC, power, and other data center needs. It simplifies operations and reduces total cost of ownership.

Closing

Kristina:

Thanks, Chris and Brooke. And thank you all for attending today. We’ll send out the recording and resources shortly. If you have future webinar topic ideas, please reach out. We’d love to hear from you. Bye for now.

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