<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Memory Efficient on SoloSoft</title><link>https://www.solosoft.dev/es/tags/memory-efficient/</link><description>Recent content in Memory Efficient on SoloSoft</description><generator>Hugo</generator><language>es-es</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.solosoft.dev/es/tags/memory-efficient/index.xml" rel="self" type="application/rss+xml"/><item><title>Unsloth: Ajuste Fino de LLMs 2x mas Rapido con Memoria Reducida</title><link>https://www.solosoft.dev/es/post/unsloth-finetuning-2026/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://www.solosoft.dev/es/post/unsloth-finetuning-2026/</guid><description>&lt;p&gt;Fine-tuning large language models on consumer hardware has been a game of memory optimization Tetris. Every byte of GPU memory is precious — model weights, optimizer states, gradients, and activations all compete for space. Parameter-efficient techniques like LoRA and QLoRA reduced the memory barrier significantly, but running these techniques efficiently required a level of CUDA optimization expertise that most developers do not have.&lt;/p&gt;
&lt;p&gt;Unsloth exists to solve this. It is an open-source library that provides drop-in optimizations for fine-tuning popular LLMs using LoRA and QLoRA. The numbers speak for themselves: 2x faster training, 50% less memory usage, and identical model output quality. The optimizations are transparent — you use the same Hugging Face APIs you already know, and Unsloth handles the low-level kernel optimization automatically.&lt;/p&gt;</description></item></channel></rss>