Understanding GPUs for AI success: Insights from our panel discussion

Last week, we hosted a panel discussion surrounding the importance of GPUs for AI success that featured Kunal Kushwaha (Field CTO), Ben Norris (AI Engineer), and Kendall Miller (Strategic Business Development). In the discussion, the panelists dove into the world of GPUs and AI, sharing their expertise and insights on the latest developments and trends in the field.

Throughout this blog, we will be summarizing some of the key discussion points made during this webinar and helping you learn more about topics such as the basics of GPUs to advanced strategies for leveraging them in AI applications. For more information on the topics shared in this blog, you can watch the full recording back here 👇

What is a GPU, and how is it different from a CPU?

The discussion began with Ben explaining that a GPU (Graphics Processing Unit), is distinct from a CPU (Central Processing Unit), in terms of its computation architecture. CPUs are designed for sequential operations, minimizing the time for single operations, whereas GPUs are built for parallel processing, making them ideal for tasks like matrix multiplications that are crucial in AI and machine learning.

The panelists also highlighted that the parallel processing capability of GPUs is what makes them so valuable for AI workloads. Ben mentioned that this is particularly relevant for tasks like model training and inference in neural networks and transformers. The thousands of cores in a GPU allow for the parallelized nature of computations that are not feasible on CPUs with fewer, but more powerful, cores.

How does the architecture of GPUs accelerate AI learning?

When looking into how the architecture of GPUs accelerates AI learning, Kunal explained that AI model training involves complex matrix operations, which GPUs are exceptionally good at due to their parallel processing capabilities. For instance, when training a model to recognize images, the image is converted into an array of numbers (features), and matrix multiplication is used to process these features. GPUs can handle these operations much more efficiently than CPUs, significantly reducing training and inference times.

Ben built upon this by discussing the importance of horizontally scaling GPUs for AI workloads. He used the example of hosting a large model, such as Llama, which requires multiple high-end GPUs (e.g., eight Nvidia H100 GPUs) to run efficiently. The ability to cluster these GPUs together and scale the infrastructure as needed is what allows for the efficient handling of large AI models.

Are all GPUs the same, and what are the key specs to compare?

The panelists discussed the differences among GPUs and the key specifications to consider. Kunal mentioned that the latest Blackwell GPUs have 16,896 CUDA cores, which represent the number of parallel tasks they can handle. Other important specs include memory (RAM) size and bandwidth, which affect how much data a GPU can hold and how quickly it can be processed. Clock speed is another factor, representing the processing speed.

We have a range of resources available to help you find the right GPU for your workload:

• A100 vs. L40s vs. H100 vs. H200 GH Superchips
• NVIDIA's B200 vs. H100

Ben added that specifications like inter-GPU communication (e.g., NVLink) are crucial for distributed inference and scaling AI workloads. The type of GPU (e.g., L40s, A100, H100, Blackwell) also matters, as each offers different capabilities and is suited for different tasks, from general AI inference to high-performance computing and model training.

What are some cost-efficient strategies for GPUs, especially for smaller AI startups?

The discussion touched on strategies for smaller AI startups to efficiently use GPUs without incurring high costs. Kunal suggested using pre-trained models and fine-tuning them for specific tasks, as well as leveraging CPU-efficient frameworks. He also recommended using cloud providers such as Civo that offer transparent and affordable pricing for GPUs.

Josh Mesout, Chief Innovation Officer at Civo, said:

"Reducing our GPU fees is about leveling the playing field for businesses. By offering the most affordable GPU pricing, we’re empowering businesses of all sizes to tap into the immense potential of AI without being held back by high infrastructure costs. We believe that access to cutting-edge technology should not be a barrier to innovation, and that every company should have the opportunity to leverage advanced and secure cloud computing technology."

Ben emphasized the value of turnkey solutions like relaxAI for startups wanting to be productive with AI quickly. relaxAI offers compatibility with the OpenAI API, making it easy to integrate into existing workflows. For those who need more control, exploring open-source models and quantization strategies can help reduce GPU usage and costs.

How does GPU memory bandwidth impact the efficiency of AI model training?

Kunal and Ben discussed the impact of GPU memory bandwidth on AI model training. Kunal explained that memory bandwidth determines how quickly data can be transferred between the GPU cores and memory. Higher bandwidth means less waiting time for data transfer, which is crucial for large AI models and data-intensive applications. Ben likened it to a highway, where a wider highway (higher bandwidth) allows more data to flow smoothly.

Ben further emphasized that memory bandwidth is as important as the amount of memory on the GPU. Strategies like KV cache management and prefix caching can help optimize bandwidth usage. For applications where latency is critical, such as high-frequency trading, having sufficient memory bandwidth is vital.

Summary

Civo is redefining GPU accessibility with a customer-first approach centered on affordability, flexibility, and seamless integration. Offering GPU rates starting at just $0.69 per hour, Civo undercuts traditional providers significantly, enabling organizations of all sizes to participate in the AI revolution.

By eliminating hidden fees such as storage and network egress charges, Civo provides transparent pricing that empowers businesses to reallocate budgets toward innovation, talent acquisition, and scaling their operations.

👉 Learn more about our research on the importance of AI in our latest whitepaper: AI for All: A Cross-Sector Study of GPU Adoption and Democratization

If you want to learn more about how Civo is helping to shape the future of the GPU landscape, check out some of these resources:

• How is Civo making AI more accessible through affordable GPUs?
• The use of AI has become the expectation, are you keeping up?
• Going Green with GPUs: The Role of Sustainability in Cloud Computing