Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2511.07885 (cs) [Submitted on 11 Nov 2025 (v1), last revised 6 Sep 2026 (this version, v6)] Title:Intelligence per Watt: Measuring Intelligence Efficiency of Local AI Authors:Jon Saad-Falcon, Avanika Narayan, Hakki Orhun Akengin, J. Demand growth strains this paradigm faster than providers can scale. Two advances create an opportunity to rethink it: small, local LMs (<=20B active parameters) now achieve competitive performance to frontier models on many tasks, and local accelerators (e.g., Apple M4 Max) can host these models at interactive latencies.
- ▪Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2511.07885 (cs) [Submitted on 11 Nov 2025 (v1), last revised 6 Sep 2026 (this version, v6)] Title:Intelligence per Watt: Measuring Intelligence Efficiency of Local AI Aut
- ▪Demand growth strains this paradigm faster than providers can scale.
- ▪Two advances create an opportunity to rethink it: small, local LMs (<=20B active parameters) now achieve competitive performance to frontier models on many tasks, and local accelerators (e.g., Apple M4 Max) can host these models at interact
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2511.07885 |
| Publication time | Mon, 14 Sep 2026 09:16:39 +0000 |
| Retrieval time | 2026-09-14T09:26:51.062Z |
| Last seen | 2026-09-14T09:26:51.062Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 7uT4uRS5pVFV · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2511.07885 (cs) [Submitted on 11 Nov 2025 (v1), last revised 6 Sep 2026 (this version, v6)] 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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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