GPU marketing focuses heavily on CUDA core counts and clock speeds. For gaming, these numbers are useful proxies. But for creative professional workloads — video editing, 3D rendering, AI/ML inference — memory bandwidth is often a more accurate predictor of performance.
What Memory Bandwidth Is
Memory bandwidth is the rate at which the GPU can read and write data to and from its VRAM — measured in GB/s. The RTX 4090, for example, has 1,008 GB/s of memory bandwidth. The RTX 4060, despite being in the same family, has 272 GB/s. That 3.7x difference in bandwidth explains much of their performance gap in bandwidth-bound workloads.
Why It Matters for Creative Work
Video editing applications like DaVinci Resolve perform colour grading operations that read and write large pixel buffers repeatedly. The speed of those operations is limited by how fast the GPU can move data to and from its memory. A GPU with higher bandwidth completes these operations faster, even if its compute units are otherwise comparable.
3D rendering with CUDA similarly involves moving large geometry and texture datasets. AI inference and training involve moving model weights and activation maps. All of these are memory bandwidth bound.
How to Compare
When comparing GPUs for creative work: look at the memory bus width (256-bit vs 128-bit) and memory type (GDDR6X vs GDDR6 vs GDDR7). A GPU with a 256-bit bus and GDDR6X will generally have significantly higher bandwidth than one with 128-bit and GDDR6, regardless of core counts.
The RTX 4070 (192-bit, GDDR6X) has 504 GB/s bandwidth. The RTX 4060 Ti (128-bit, GDDR6) has 288 GB/s. In GPU-compute-bound creative applications, that bandwidth gap shows up clearly in benchmarks.