<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Local LLM on SwiftTribune</title><link>https://swifttribune.walidsassi.com/tags/local-llm/</link><description>Recent content in Local LLM on SwiftTribune</description><image><title>SwiftTribune</title><url>https://swifttribune.walidsassi.com/images/og-default.svg</url><link>https://swifttribune.walidsassi.com/images/og-default.svg</link></image><generator>Hugo -- 0.147.8</generator><language>en-us</language><lastBuildDate>Sun, 17 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://swifttribune.walidsassi.com/tags/local-llm/index.xml" rel="self" type="application/rss+xml"/><item><title>MLX Swift &amp; On-Device AI with Adrien Grondin</title><link>https://swifttribune.walidsassi.com/podcast/mlx-swift-on-device-ai-adrien-grondin/</link><pubDate>Sun, 17 May 2026 00:00:00 +0000</pubDate><guid>https://swifttribune.walidsassi.com/podcast/mlx-swift-on-device-ai-adrien-grondin/</guid><description>Adrien Grondin on MLX Swift, running Mistral, Qwen and Gemma locally on Apple Silicon, how MLX differs from Core ML and Foundation Models, and the real limits of on-device inference: memory, battery, latency.</description></item></channel></rss>