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A New Metric Reveals Local AI's Viability: "Intelligence Per Watt" Skyrockets 5.3x in Two Years

STANFORD, CA — The explosive demand for Large Language Models (LLMs) is pushing centralized cloud infrastructure to its limits, consuming vast amounts of energy and compute resources. But a new study from Stanford University researchers suggests a path toward sustainable AI: shifting the bulk of inference workloads from massive data centers to personal devices.

The paper introduces a unified metric, Intelligence Per Watt (IPW), designed to quantify the true efficiency of local AI. IPW is defined as task accuracy delivered per unit of power consumption. It provides a direct measure of whether small, power-constrained devices—like laptops with powerful integrated chips—can viably handle real-world AI tasks while remaining competitive with frontier cloud models.

The findings, based on a massive empirical study profiling over 20 state-of-the-art local LMs and eight accelerators across one million real-world queries spanning 2023–2025, confirm that local AI is rapidly closing the gap.

Local Models Achieve 88.7% Coverage

The analysis reveals that highly optimized local LMs (models under 20 billion active parameters, such as QWEN3 or GPT-OSS variants) can successfully handle 88.7% of typical single-turn chat and reasoning queries.

The feasibility varies significantly by domain: models excel at creative and conversational tasks, achieving over 90% coverage for queries related to Arts & Media. However, coverage drops to 68% for technically specialized disciplines, such as Architecture and Engineering, which still benefit from the massive scale of frontier cloud models.

IPW Improvement Outpaces Expectation

Crucially, the efficiency of local inference is improving at a breakneck pace. The study found that Intelligence Per Watt has surged by 5.3x between 2023 and 2025.

This remarkable efficiency leap is driven by a compounding effect of both software and hardware improvements. Algorithmic advancements in model architectures and training (yielding models like GPT-OSS-120B) account for a 3.1x gain in IPW, while corresponding progress in local accelerators (such as the Apple M4 Max) delivered an additional 1.7x efficiency boost.

This combined progress has dramatically expanded the fraction of queries that can be reliably serviced locally, rising from just 23.2% in 2023 to 71.3% today.

Hybrid Routing Delivers Massive Savings

The practical implications for cloud strain are profound. The researchers modeled a hybrid local-cloud system where a smart router directs simple queries to local devices and sends complex requests to the cloud.

Simulations show that a practical router, achieving 80% accuracy in determining the minimal model capable of solving a query, can reduce energy consumption by 64.3%, compute usage by 61.8%, and cost by 59.0% compared to a cloud-only approach. For current platform-scale inference demands—billions of queries daily—these savings translate into terawatt-hours of annual energy reduction.

These results establish local AI as a practical and fast-growing complement to centralized cloud infrastructure. As model and accelerator efficiency continues to soar, Intelligence Per Watt will remain the critical metric tracking the transition toward a more decentralized and sustainable AI ecosystem.