<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LLaMA.cpp on The Coders Blog</title><link>https://thecodersblog.com/tag/llama.cpp/</link><description>Recent content in LLaMA.cpp on The Coders Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 08 May 2026 17:37:15 +0000</lastBuildDate><atom:link href="https://thecodersblog.com/tag/llama.cpp/index.xml" rel="self" type="application/rss+xml"/><item><title>LLaMA.cpp: Multi-Token Prediction Boosts Gemma 4 Speed</title><link>https://thecodersblog.com/multi-token-prediction-speedup-for-llama-cpp-2026/</link><pubDate>Fri, 08 May 2026 17:37:15 +0000</pubDate><guid>https://thecodersblog.com/multi-token-prediction-speedup-for-llama-cpp-2026/</guid><description>&lt;p&gt;The dream of truly responsive, local Large Language Models (LLMs) has always been hampered by the fundamental latency of sequential token generation. Every word, every punctuation mark, requires a full forward pass through the neural network. For developers striving to integrate LLMs into real-time applications – think coding assistants that don&amp;rsquo;t lag, interactive storytelling engines, or instant summarization tools – this inherent bottleneck can be a deal-breaker. Enter LLaMA.cpp, the ever-evolving powerhouse for running LLMs efficiently on consumer hardware. Its latest advancement, Multi-Token Prediction (MTP), is not just another optimization; it&amp;rsquo;s a fundamental shift in how we can accelerate single-stream LLM generation, and early indicators suggest it&amp;rsquo;s a game-changer, particularly for models like Gemma 4.&lt;/p&gt;</description></item></channel></rss>