Revolutionizing AI with Local Inference: Key Findings from ‘Intelligence per Watt’
In a rapidly evolving AI landscape, our latest research highlights a transformative shift from centralized cloud infrastructures to local accelerators. The paper “Intelligence per Watt: Measuring Intelligence Efficiency of Local AI” reveals groundbreaking insights:
- Local Model Competitiveness: Small language models (≤ 20B parameters) demonstrate competitive performance, accurately answering 88.7% of real-world queries.
- Improvement Metrics: From 2023 to 2025, intelligence per watt (IPW) improved by 5.3x, with local query coverage soaring from 23.2% to 71.3%.
- Efficiency Gains: Local accelerators outperform cloud counterparts with at least a 1.4x lower IPW.
These findings underscore the potential of local inference to redistribute demand effectively. Our unique IPW profiling harness enables systematic benchmarking, paving the way for future advancements.
Curious to explore how local inference can reshape AI? Dive into our research and share your thoughts! 📊💡
