Definition #
The systematic measurement of AI systems against predefined metrics (accuracy, latency, energy use) to compare models or track improvements.
Key Characteristics #
- Hardware-aware benchmarks (GPU vs TPU)
- Domain-specific suites (medical imaging, NLP)
- Open leaderboards (MLPerf, Hugging Face)
- Cost-efficiency metrics (FLOPs per prediction)
Why It Matters #
Helps avoid vendor lock-in—AWS Inferentia chips show 40% better $/inference than GPUs in MLPerf tests.
Common Use Cases #
- Cloud AI service selection
- Model optimization prioritization
- Academic paper comparisons
Examples #
- MLPerf Industry Standard
- Hugging Face LLM Leaderboard
- DAWNBench for training efficiency
FAQs #
Q: What’s the “benchmarking tax”?
A: Over-optimizing for benchmarks can reduce real-world performance (focus on task-specific metrics).
Q: How to benchmark ethically?
A: Report full environmental impact (carbon emissions) alongside speed/accuracy.