GPU & CPU Compute Performance Forecast

Forecasting Methodology

Forecasts here apply log-linear regression to historical performance data in each category (GPU / CPU).

ln(y) = α + β · x + ε
ŷ(x) = exp(α + β · x)

Prediction interval: exp( ln(ŷ) ± t_α/2 · s · √(1 + 1/n + (x − x̄)² / Σ(xᵢ − x̄)²) )

y: Peak compute performance (TFLOPS, FP32)
x: Year of release
s: Residual standard error (log scale)
n: Number of data points
68% prediction interval: z ≈ 1.0 — "roughly within this range"
95% prediction interval: z ≈ 1.96 — "almost certainly within this range"

Prediction intervals widen into the future, reflecting growing forecast uncertainty. GPU and CPU trend lines are regressed independently. Data is approximated from manufacturer official specifications and various benchmark sources.

* This forecast extrapolates from past trends and does not account for physical limits, architectural innovation, or market dynamics. Actual performance may diverge significantly.

Compute Performance Trends &
Future Forecast

Forecasting Methodology

Data Table

Compute Performance Trends & Future Forecast

Forecasting Methodology

Data Table

Compute Performance Trends &
Future Forecast