Memory Bandwidth measures how much data a GPU can move between its chip and video memory per second, expressed in GB/s. Formula: Memory Frequency × Bus Width × 2 ÷ 8.
Why it matters:
High-res gaming (4K, 8K)
Ray tracing & shaders
AI/ML training
Rendering & video editing
It also impacts operational costs in big ways:
Efficiency saves money: lower power = lower electricity and cooling bills.
Scaling: more GPUs per rack when each runs cooler.
Sustainability: less heat, less carbon footprint.
So beyond raw performance, bandwidth efficiency shapes how affordable and sustainable GPU computing really is.
I recently ran Memtest Vulkan v0.5.0 on an NVIDIA GeForce RTX 3070 Laptop GPU to check memory stability and performance. The standard 5-minute test completed successfully without errors, confirming stable VRAM under Vulkan workloads.
I ran CrystalMark Retro on an ASUS ROG Strix G713QR (Ryzen 9 5900HX, 8c/16t, Zen 3) to see how this 2021-era CPU holds up in 2025.
Specs:
CPU: AMD Ryzen 9 5900HX (Zen 3, 8c/16t)
GPU: Integrated Radeon Graphics (RTX disabled)
RAM: 32 GB
OS: Windows 10 Home
Results:
Multi-Core: 83,684
Single-Core: 9,847
Storage (Sequential): 8,677 read / 8,305 write
2D Graphics: ~9–10K
3D Graphics (iGPU): Wireframe 4,239 / Polygon 2,847
The CPU still delivers strong multi-core performance, and NVMe speeds are respectable. 3D performance is limited on the iGPU, as expected, but I’ll be re-running with the RTX GPU enabled for comparison.
Curious - does anyone else still benchmark their older hardware with tools like this?
Publicly available manufacturer specifications for 1,800+ NVIDIA, AMD, and Intel GPUs to visualize the evolution of single-precision floating-point performance (GFLOPS) over the past 18 years.
Highlights:
GPU performance grew from ~497 GFLOPS in 2007 to over 100,000 GFLOPS in 2025
Major performance leaps occurred with microarchitectural shifts.
Methodology:
GFLOPS calculated from shader counts and core clock speeds:
GFLOPS = (Shader Units × Core Clock × 2) / 1,000,000,000
Why it matters:
High-res gaming (4K, 8K)
Ray tracing & shaders
AI/ML training
Rendering & video editing
It also impacts operational costs in big ways:
Efficiency saves money: lower power = lower electricity and cooling bills.
Scaling: more GPUs per rack when each runs cooler.
Sustainability: less heat, less carbon footprint.
So beyond raw performance, bandwidth efficiency shapes how affordable and sustainable GPU computing really is.
Interactive Chart GPU Memory Bandwidth Evolution (2007–2025) analysis