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[2511.07885] Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
Summary
The paper presents a metric called Intelligence per Watt (IPW) to evaluate the efficiency of local AI models compared to centralized cloud systems, highlighting significant improvements in local inference capabilities.
Why It Matters
As demand for AI processing grows, understanding the efficiency of local AI models versus centralized cloud systems is crucial for sustainable technology development. This research provides insights into optimizing AI deployment, potentially reducing reliance on cloud infrastructure and enhancing performance on power-constrained devices.
Key Takeaways
- Local language models can achieve 88.7% accuracy on real-world queries.
- The Intelligence per Watt (IPW) metric improved by 5.3x from 2023 to 2025.
- Local accelerators outperform cloud counterparts in efficiency, indicating potential for optimization.
Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2511.07885 (cs) [Submitted on 11 Nov 2025 (v1), last revised 26 Feb 2026 (this version, v3)] Title:Intelligence per Watt: Measuring Intelligence Efficiency of Local AI Authors:Jon Saad-Falcon, Avanika Narayan, Hakki Orhun Akengin, J. Wes Griffin, Herumb Shandilya, Adrian Gamarra Lafuente, Medhya Goel, Rebecca Joseph, Shlok Natarajan, Etash Kumar Guha, Shang Zhu, Ben Athiwaratkun, John Hennessy, Azalia Mirhoseini, Christopher Ré View a PDF of the paper titled Intelligence per Watt: Measuring Intelligence Efficiency of Local AI, by Jon Saad-Falcon and 14 other authors View PDF HTML (experimental) Abstract:Large language model (LLM) queries are predominantly processed by frontier models in centralized cloud infrastructure. Rapidly growing demand strains this paradigm, and cloud providers struggle to scale infrastructure at pace. Two advances enable us to rethink this paradigm: small LMs (<=20B active parameters) now achieve competitive performance to frontier models on many tasks, and local accelerators (e.g., Apple M4 Max) run these models at interactive latencies. This raises the question: can local inference viably redistribute demand from centralized infrastructure? Answering this requires measuring whether local LMs can accurately answer real-world queries and whether they can do so efficiently enough to be practical on power-constrained devices (i.e., laptops). We propose intelligence per watt (IPW), ...
