Fine-Tuning Gemma 4 on an 8 GB RTX 3050: Slow, Low End and Still Viable
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…
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.
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