---
format: "aidr-story-markdown/v1"
id: "c4bfc91a7a6a4a15a54197fd496d83b8eeb8242d3b05512b05cdaa2914a07fd5"
canonical_url: "https://aidr.today/c4bfc91a?lang=en"
title: "AIR-LLM: Broadcasting AI Weights over Radio for Memory-Free Edge LLM Inference via RF Computing"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
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published_at: "2026-10-14T08:45:12.000Z"
category: "Research"
topics: ["inference"]
source_urls: ["https://arxiv.org/html/2610.00465","https://lobste.rs/s/h8tdxj/air_llm_broadcasting_ai_weights_over"]
summary: "AIR-LLM is a new LLM inference architecture that broadcasts model weights over radio frequencies, allowing edge devices to perform inference without storing or loading weights locally."
---

# AIR\-LLM: Broadcasting AI Weights over Radio for Memory\-Free Edge LLM Inference via RF Computing

> [Open the canonical story](<https://aidr.today/c4bfc91a?lang=en>)

**Published:** 2026-10-14T08:45:12.000Z
**Category:** Research
**Topics:** inference

## Summary

AIR\-LLM is a new LLM inference architecture that broadcasts model weights over radio frequencies, allowing edge devices to perform inference without storing or loading weights locally\.

## Sources

- [Story source](<https://arxiv.org/html/2610.00465>)
- [Discussion](<https://lobste.rs/s/h8tdxj/air_llm_broadcasting_ai_weights_over>)