{
  "id": 15929328,
  "title": "Fine-Tuning Gemma 4 on an 8 GB RTX 3050: Slow, Low End and Still Viable",
  "url": "https://urgent.news/2026/10/21/fine-tuning-gemma-4-on-an-8-gb-rtx-3050-slow-low-end-and-still-viable",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-10-21T18:30:33.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/xbill/fine-tuning-gemma-4-on-an-8-gb-rtx-3050-slow-low-end-and-still-viable-291f"
  },
  "original_language": "en",
  "account": "This article offers a detailed guide on fine-tuning Gemma 4 E2B on an 8 GB NVIDIA RTX 3050, a budget desktop GPU. The model's performance is measured on the GSM8K dataset, with results compared to llama.cpp's built-in thinking cap of 256 tokens, which reduces token usage by 35.8% without any training. The guide demonstrates how an 8 GB RTX 3050 can perform inference and fine-tuning, running the whole loop overnight and delivering a trained model in the same 4-bit file it serves. The project includes a trainer that trains on the same GPU, an exporter that writes trained weights back into the file, and a comparison with the standard QLoRA recipe. The article also includes a demo showing the performance of the tuned models on the 250 held-out GSM8K problems.",
  "summary": "This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady This article provides a step by step guide to fine-tuning Gemma 4 E2B on an 8 GB NVIDIA RTX 3050, the low end of a desktop GPU, so that the model thinks less before it answers. Every log, script and number is committed. https://github.com/xbill9/lazy-gemma The RTX 3050 is an 8 GB inference card: it serves…",
  "key_points": [
    "Fine-tuning Gemma 4 on 8 GB RTX 3050 is slow and low-end",
    "Model performance measured on GSM8K dataset",
    "Overnight inference and fine-tuning deliver 4-bit trained model"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}