Methodology

How we calculate the GPU Value Score and normalize market data.

How the Performance Index is built

A GPU's raw speed comes from several public benchmark suites — 3DMark Time Spy, 3DMark Steel Nomad and PassMark — but they don't agree on a scale and they don't all test every card. Instead of trusting one of them, we line them all up onto a single scale and blend them, so one number ranks every GPU fairly even when the cards were measured by different tests.

One fixed yardstick

Every card is measured relative to a single reference GPU, the GeForce RTX 3060 12GB, which we pin at 100. Multiplying by 87.35 restores the familiar 3DMark-style number you see on the site (the 3060 lands on 8,735). Because the yardstick never changes, scores stay comparable across generations.

Line up each benchmark

Benchmarks disagree in a predictable way — some, like PassMark, squeeze the gap between fast and slow cards, while gaming-style tests spread it out. We learn each benchmark's stretch from the overlap between cards and undo it, so a score from any test lands at the same place on the shared scale.

Blend by trust, fill the gaps

The lined-up scores are combined with a weighted average: gaming-style benchmarks count most, synthetic ones less. A card that's missing one benchmark is still scored fairly from the others, so partial coverage never inflates or deflates its rank. Peak FP32 TFLOPS is used only as a last resort, when no real benchmark exists for a card.

Scale to 0–100% for easy reference

The steps above produce a raw 3DMark-style index. For the Performance figure shown on GPU and listing pages, we normalize that index to a 0–100% scale: the fastest GPU in our database sits at 100%, and every other card is shown as its percentage of that top score. This makes relative speed easy to read at a glance — a card at 72% delivers roughly 72% of the fastest card's measured performance.

Show the math▶

We treat each benchmark as a noisy measurement of one hidden quantity: a GPU's true log-performance θ (theta), with the reference GPU pinned at θ = 0. In log space, each source s relates to θ by its own straight line, where the slope bₛ captures how strongly that benchmark stretches or compresses the performance range:

ln(score_g,s) ≈ a_s + b_s · θ_g

We fit each line from the data — pivoted through the reference for benchmarks the reference itself ran, ordinary least squares otherwise — then invert it to map any measured score back onto the shared θ axis:

θ̂_g,s = ( ln(score_g,s) − a_s ) / b_s

Finally we fuse a card's available sources with a weighted average on the θ axis (weights wₛ are editorial trust levels), convert back out of log space, and rescale to the historical 3DMark range:

Index_g = 100 · exp( Σ w_s·θ̂_g,s / Σ w_s )
perf_index = round( Index_g × 87.35 )

Note: Time Spy defines the scale (slope fixed at 1) for continuity with the historical index, so this is a pragmatic calibration rather than a pure latent-variable estimate that would privilege no single benchmark. Each source's residual scatter is tracked as a confidence signal but does not change the blend weights.

Value Score

Value Score = (Performance Index / Price)

Our unique Value Score identifies the 'sweet spot' in the market. It represents the amount of performance you receive for every dollar spent. A higher score indicates better value.