5 Misconceptions about GPUs

Artificial intelligence and machine learning are transforming industries, enabling breakthroughs in fields such as healthcare by assisting in early disease detection and powering real-time decision-making in autonomous vehicles. AI/ML technologies are allowing us to use natural language processing for speech recognition and translation, and develop predictive models for climate change and weather forecasting. These advancements are powered by graphics processing units (GPUs), which use parallel processing to efficiently handle complex calculations.

Historically, GPUs were designed primarily for rendering graphics in video games and visual applications, focusing on parallel processing for tasks like creating images and handling visual effects. However, as the demand for high-performance computing grew, especially in areas like deep learning, scientific simulations, and data processing, the architecture of GPUs was found to be well-suited for these tasks due to their ability to handle a large number of operations simultaneously. Manufacturers like NVIDIA, AMD, Intel, and others recognized this demand and adapted GPU designs, introducing specialized cores (like CUDA Cores and Tensor Cores) to optimize them for deep learning. Despite emerging in the 1970s, several misconceptions about GPUs and their evolving capabilities remain. These misunderstandings often lead to confusion, especially when selecting the right tooling for AI and ML projects. Let’s clear up some of the most common misconceptions surrounding GPUs.

Summary

1. GPUs used in AI and ML advancements offer powerful parallel processing for complex calculations.

2. Common misconceptions about GPUs include thinking more VRAM equals better performance, that any GPU can handle AI workloads, and that CPUs don’t matter when using a powerful GPU.

3. DigitalOcean GPU Droplets provide an accessible, flexible, and cost-effective way to run AI/ML workloads, making high-performance computing available to developers, startups, and small businesses.

5 Misconceptions about GPUs

For a long time, GPUs were primarily seen as gaming tools designed to handle the complex visual rendering required for high-performance games. Beyond this, there are other misconceptions surrounding GPUs, from their cost-effectiveness to their capabilities in non-visual tasks such as data analysis.

1. More VRAM = better performance

GPU buyers may believe that more video random access memory (VRAM) always results in better performance. This thinking stems from the notion that similar to the RAM on your PC, more VRAM must mean more power. People often prioritize VRAM specs when choosing a GPU, thinking it’s the ultimate performance indicator.

While VRAM is important, it’s only part of the equation. VRAM mainly stores assets like textures and frame buffers, making it useful for high-resolution graphics or large datasets. However, extra VRAM doesn’t automatically improve GPU speed if your workload doesn’t need that much memory. For example, in tasks like training small to medium-sized machine learning models, a GPU with 8GB of VRAM can perform just as well as a 12GB one if the model doesn’t require the extra memory. The additional VRAM would go unused, and other factors like GPU core power, clock speed, bandwidth, and architecture—including specialized components like new generation Tensor cores—would determine performance. If the workload doesn’t need more memory, the extra VRAM won’t provide a boost.

However, having more VRAM can be beneficial for future-proofing, allowing work with larger models and datasets as they continue to grow in size and complexity. For specific tasks like training very large language models or processing high-resolution images, ample VRAM can determine whether a model can be trained on a single GPU.

2. GPUs are only for large enterprises and advanced users

GPUs aren’t reserved for projects with heavy workloads or tech experts dealing with complex tasks like machine learning or 3D rendering. This misconception probably comes from the early days when GPUs were expensive and mainly used for professional workstations or large-scale computing. As a result, some might assume GPUs are excessive unless you’re in a highly specialized field or running a massive enterprise.

In reality, GPUs are adaptable and accessible to everyone, from solo developers and small startups to companies of any size. For example, DigitalOcean GPU Droplets offer a flexible, scalable solution that fits projects of all sizes — whether you’re launching an AI-powered startup, building an AI business, or experimenting with your next AI side project. With DigitalOcean’s transparent pricing model and cloud tools integration, you need not invest in costly GPU infrastructure. You can run your workloads on cloud-based GPU instances, paying only for what you use and scaling up or down based on your needs.

3. Any GPU can handle an AI/ML workload

It’s easy to assume that if a GPU handles everyday computational tasks well, it should be able to tackle AI or deep learning workloads, too, but these demands are fundamentally different. General-purpose GPUs are built for a wide range of applications, but AI tasks require specialized hardware to perform intensive matrix calculations and manage vast datasets efficiently. While some AI tasks can still be run on general-purpose GPUs, it will be much slower and less efficient.

For example, suppose you’re developing a predictive analytics tool for financial markets or training a machine learning model for medical imaging. In that case, the computational demands far exceed what most general-use GPUs can manage. GPUs explicitly designed for AI workloads, such as those equipped with CUDA and Tensor Cores, are built to handle the intensive matrix operations and large datasets that these tasks require. Whether you’re working on large-scale data analysis or deep learning applications, these specialized GPUs provide the necessary high-performance infrastructure to process complex models efficiently without hitting performance bottlenecks. Another important point to consider is the capability gap between NVIDIA (with its advanced AI-optimized hardware), and other GPU providers continues to grow, further emphasizing the importance of choosing the right hardware for AI tasks. However, as the field evolves, the distinction between general-purpose and AI-specific GPUs may become less pronounced.

4. CPU doesn’t matter when using a powerful GPU

Many believe the CPU becomes less critical if you have a high-end GPU. The assumption is that the GPU will do all the heavy lifting, so the CPU doesn’t play a significant role. This leads some to invest in a top-tier GPU while overlooking the CPU altogether.

The CPU and GPU work together, and if your CPU can’t keep up, it creates a bottleneck that prevents the GPU from performing to its full potential, no matter how powerful it is. The CPU handles tasks like game logic, non-player character artificial intelligence (NPC AI), and physics calculations in gaming or managing data and instructions in other intensive applications. If it’s too slow, the GPU has to wait, and your overall system performance drops.

For example, even the best GPU can’t compensate for a weak processor in CPU-intensive applications such as data analytics, video editing, or scientific simulations. A mismatched setup will leave your system underperforming. Both need to be well-matched for optimal performance. CPU-GPU partnership is like a relay race—if the first runner (CPU) is slow, the second runner (GPU) can’t compensate for the lost time. When building or upgrading a system, ensure the CPU can handle the tasks you’ll be running, or the GPU won’t have the chance to show its power.

5. More cores mean more speed

In GPUs, the term “core” refers to the GPU’s processing units, commonly called CUDA cores (in NVIDIA GPUs) or stream processors (in AMD GPUs). The idea of having more GPU cores directly translates to faster task performance. The assumption here is that more cores allow for more simultaneous processing, which might seem logical at first glance. For instance, if a GPU has thousands of cores compared to a CPU’s few dozen, it’s easy to think that the GPU will always be faster because it can handle more operations at once.

While high core counts are advantageous for parallel tasks like image rendering and deep learning, many applications may not be designed to use multiple cores effectively, leading to diminishing returns. For example, a video editing application primarily relies on sequential processing for tasks like applying filters or rendering effects. In this case, even if a GPU has thousands of cores, the software may only utilize one or two at a time. As a result, the performance gains from having more cores are minimal because the application cannot take advantage of the parallel processing capabilities of the GPU.

While a higher core count in GPUs can potentially lead to better performance, it’s not always a direct relationship. Other factors such as core efficiency, GPU architecture, memory bandwidth, clock speeds, and software optimization play crucial roles in determining overall performance. Newer microarchitectures, such as NVIDIA’s Ampere or Hopper, may outperform older ones like Volta or Pascal, even with similar core counts, due to improvements in core design, memory handling, and task-specific optimizations. In some cases, a GPU with fewer but more efficient cores and modern architecture can outperform one with a higher core count if the workload doesn’t fully use the extra cores.

To effectively use the available cores, consider software optimization techniques such as parallelism and load balancing, as poorly optimized programs may fail to use all available cores effectively. When you choose a GPU, understand the nature of your task and consider both the core count and architecture offered by technology companies. A GPU with fewer but more powerful cores and solid architecture can outperform one with a higher core count if your workload doesn’t fully use the extra cores.

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