Teraflops Are a Bad Way to Compare GPUs: Here’s Why

By on June 12, 2023

MSI GeForce RTX 4070 TI Ventus 12G OC GPU in a gaming PC at CES 2023

Teraflops provide a simplistic view of GPU performance, but fail to account for differences in architecture, efficiency, and software optimizations. Real-world performance testing and understanding of architecture specifics provide a more accurate comparison of GPUs.

Teraflops, often hailed as the ultimate GPU comparison metric, have limitations. This oversimplification doesn’t capture GPU complexity, masking their true performance. Instead, real-world performance testing, nuanced understanding of architecture, and context-specific uses are what matter.

What is a Teraflop?

A teraflop is a unit of computing speed that equates to a trillion (1012) floating-point operations per second. In the world of graphics processing units (GPUs), teraflops are often used as a measure of performance. Essentially, the higher the teraflop count, the more calculations a GPU can handle in a second, supposedly leading to better performance.

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Teraflops are derived from the hardware specifications of a GPU, primarily the core clock speed, the number of cores, and the number of operations per cycle. It’s an easy-to-understand number, but like any oversimplified metric, it falls apart when misused.

When Teraflops Are Good for GPU Comparisons

Teraflops can be helpful when comparing GPUs of the same architecture and generation. Since these GPUs are built using the same technology, they generally predictably scale their performance with their teraflop count.

For instance, if you compare two graphics cards from the same NVIDIA RTX 3000 series, the one with the higher teraflop count will generally perform better. This is because these GPUs are designed similarly, and any performance differences can be largely attributed to their processing power, which is represented by the teraflop count.

Why Teraflops are Bad For GPU Comparisons

However, teraflops become a much less reliable performance indicator when comparing GPUs across different architectures or generations. The primary issue here is that not all flops are created equal.

The way a GPU uses its teraflops can vary significantly based on its architecture. For instance, an NVIDIA GPU uses its teraflops differently than an AMD GPU, resulting in different performance levels despite similar teraflop counts. Similarly, a modern GPU will use its teraflops more effectively than an older one, even if they have the same count.

In other words, teraflops only tell part of the story. They don’t account for differences in efficiency, memory bandwidth, or driver optimizations that can significantly impact performance.

GPUs are Working Smarter, Not Harder

Today’s GPUs are becoming increasingly complex and intelligent. They don’t just blindly perform calculations—they work smarter.

For instance, GPUs now feature technologies like NVIDIA’s DLSS and AMD’s FidelityFX Super Resolution, which use AI to upscale lower-resolution images in real-time, improving performance without noticeably decreasing visual quality. These technologies can greatly enhance the performance of a GPU, and they have nothing to do with teraflops.

Similarly, advancements in architecture, such as better parallel processing and memory management, can significantly improve GPU performance. Again, these improvements aren’t reflected in the teraflop count.

Fudging the TFLOP Numbers

Another issue with using teraflops to compare GPUs is that the numbers can be manipulated. Manufacturers might “boost” their teraflop counts by increasing the core clock speed or the number of cores.

However, these boosts often don’t translate to real-world performance improvements, as they can lead to increased power consumption and heat generation, which can throttle the GPU and lower performance. Alternatively, while there is an increase in performance, it’s not directly proportional to the rise in (theoretical) TFLOPs, because of constraints in the GPU’s architecture, such as memory bandwidth bottlenecks or limited GPU cache.

The Right Way to Compare GPUs

So, if teraflops aren’t a reliable way to compare GPUs, what is? The answer is simple: real-world performance testing.

Performance benchmarks, such as those performed by independent reviewers, provide the most accurate measure of a GPU’s performance. They involve running the GPU through a series of tasks or games and measuring its performance.

When looking at benchmarks, it’s important to consider the specific tasks or games you’ll use the GPU for. A GPU might excel at one task but perform poorly at another, so check benchmarks relevant to your use case.

Also, consider other factors such as power consumption, heat output, and cost. A GPU might have excellent performance, but it might not be your best choice if it’s too power-hungry or expensive.

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