Benchmark Methodology

Transparency is critical when helping you choose hardware. Here's exactly how we calculate, verify, and present performance data.

When real benchmark data isn't available, we use this formula:

tokensPerSec = (GPU_cores / 10,000) × 50 × model_penalty × quant_multiplier

• Base Speed: GPU compute power (CUDA cores / stream processors). RTX 4090 baseline: ~82 tokens/sec; RTX 4080 baseline: ~49 tokens/sec.
• Model Penalty: Larger models process slower—7B ≈ 100% base, 70B ≈ 40–60%, 180B ≈ 20–30%.
• Quantization Multiplier: Q4 = 100%, Q8 = 70%, FP16 = 40%.
• Randomness: ±10% variation to simulate real-world variance.

Estimated benchmarks can be off by 40% or more. That's why we're building a community-driven benchmark database.

Help make this data better for everyone. When you submit a benchmark:

1. We verify it against expected ranges
2. Moderators review your test conditions
3. If approved, it replaces the estimate on the relevant pages
4. Your username gets credited on the benchmark
5. You earn reputation points (coming soon)

• Hardware Specifications: TechPowerUp GPU Database (industry standard reference)
• AI Model Information: HuggingFace Model Hub (official source)
• Pricing Data: Amazon, Newegg, Best Buy, Micro Center (updated weekly)
• Benchmark Data: Community submissions, Reddit (r/LocalLLaMA), Discord (llama.cpp), GitHub issues

We're continuously improving our methodology:

• ✅ Better estimation algorithms (analyzing 500+ real benchmarks)
• ✅ Automated outlier detection
• 🔄 Hardware compatibility warnings (VRAM checks)
• 🔄 Power consumption estimates
• 🔄 Thermal throttling predictions
• 📅 Multi-GPU configurations (coming Q1 2026)
• 📅 CPU bottleneck analysis (coming Q2 2026)

Questions about our methodology? Contact us