Data sources & methodology
Where the numbers come from. GPU Hunter aggregates published benchmark data from community testing — we don't run our own benchmarks.
How we source data
GPU Hunter is an aggregation index, not a testing lab. We collect inference benchmark results from published community sources — primarily llama.cpp GitHub discussions, hardware review sites, and public leaderboards. We normalize this data into a consistent format so you can compare GPUs across vendors without digging through dozens of threads and spreadsheets.
Where the benchmarks come from
Our primary benchmark data comes from llama.cpp community testing threads on GitHub, where contributors run standardized inference tests and share results publicly.
Additional references
We cross-reference primary data with hardware review sites and aggregated leaderboards to fill gaps and validate results.
What the sources measure
The community benchmarks we source typically use the following methodology. Exact parameters vary by contributor and hardware platform.
How we process the data
Raw benchmark numbers from different sources aren't directly comparable — different models, quantizations, and context lengths produce different results. We map published results into a common editorial index and label the displayed speed as a representative estimate. The score also weighs memory capacity and a dated reference price. Results from different backends, models, drivers, and contexts are not laboratory-equivalent, so close rankings are directional.
Hardware prices
Prices are editorial reference estimates captured on April 30, 2026 — MSRP or launch pricing for some new cards and approximate used-market observations for older hardware. They are not live offers, inventory claims, or guaranteed transaction prices. Always verify the exact model, condition, seller, and price before purchasing.
Quantization levels
We track three quantization levels to cover the full spectrum of quality vs. speed tradeoffs.
4-bit quantization with K-means optimization. Best speed-to-quality ratio for most users. ~60% smaller than FP16.
8-bit quantization. Minimal quality loss vs. full precision. ~50% smaller than FP16. Good for tasks requiring high accuracy.
Full 16-bit floating point. No quantization loss. Requires the most VRAM. Use when you need exact model fidelity.
Research behind the methodology
These research clusters explain why GPU Hunter weighs VRAM, memory bandwidth, quantization format, and runtime support instead of ranking GPUs by raw TFLOPS alone.