A teraflop (TFLOP) is a unit of computational performance — specifically, one trillion (10¹²) floating‑point operations per second. Essentially, it’s a measure of how many complex math calculations (involving decimal-based numbers) a processor — particularly a GPU or supercomputer — can perform in a single second.

1. What does a teraflop tell us?

• It represents the peak theoretical speed of a processor’s floating-point computations.
• For example, a GPU with 10 TFLOPs can theoretically perform 10 trillion operations per second.

2. Contextual examples:

• Xbox Series X GPU: ~12 TFLOPs
• PlayStation 5 GPU: ~10.28 TFLOPs
• These numbers indicate raw computational capacity.

3. Limitations of relying on TFLOPs alone:

• Not all architectures are equal: Different GPU designs with the same TFLOP rating can deliver vastly different real-world performance.
• Bottlenecks matter: Actual performance depends on CPU speed, memory bandwidth, software optimization, ray tracing abilities, etc..
• Marketing vs reality: TFLOPs are often used in marketing, but don’t always reflect gaming or application performance .

4. When are teraflops useful?

• Comparing same-architecture GPUs: If two GPUs share the same core technology and generation, the one with higher TFLOPs typically performs better.
• Rouge comparison limit: Mixing different architectures (e.g., NVIDIA vs AMD, or consoles vs PCs) makes TFLOP-based comparisons less meaningful .

🧭 Practical takeaway:

• Think of TFLOPs like a car’s horsepower: Higher power, but doesn’t guarantee speed without efficient design, good “transmission” (software/drivers), and proper system balance.
• Check real-world benchmarks — especially for gaming or productivity — along with TFLOP figures for a complete picture.

🔎 Quick summary table