Guide

Consumer Hardware Performance Guide

Consumer hardware performance is mostly a story about fit. Once a model fits cleanly in VRAM, speed tends to improve and the experience becomes easier to predict. When it does not fit, every extra trick you need to make it work adds friction.

Section 1

How VRAM changes the experience

The difference between an 8GB GPU and a 24GB GPU is not only speed. It changes which models fit, how much context you can keep live, and whether you spend your time waiting on memory management instead of getting work done.

  • 8GB: best for compact models and aggressive quantization.
  • 12GB to 16GB: a better balance for mainstream local workflows.
  • 24GB+: enough headroom for larger models and fewer compromises.
Compare consumer GPUs
See how VRAM and average speed line up in practice.
Open the benchmark table
Look at individual results across hardware and models.

Section 2

Performance trends to expect

As VRAM rises, you typically gain the freedom to run stronger models without falling back to a memory-constrained setup. Speed still matters, but the biggest wins often come from removing the bottleneck that was forcing you into a smaller model class.

  • Better fit usually improves responsiveness more than small clock bumps.
  • Model choice changes faster than GPU choice, so benchmark both sides.
  • A balanced system often beats a fast GPU paired with an oversized model.
Explore model coverage
See how the same model performs across many submissions.

Section 3

How to read the data

Use the hardware page to compare average speed, model size, and VRAM tier together. That combination tells you whether a GPU is a practical choice for the models you care about or just a theoretical winner on paper.

  • Start with the VRAM tier that fits your target models.
  • Check average speed for real-world responsiveness.
  • Use the benchmark results page to inspect the underlying runs.