This article provides a step by step guide to running Gemma 4 E2B, Google's quantization-aware trained (QAT) build, on a 4 GB NVIDIA GTX 1650 Ti laptop GPU with plain PyTorch and Hugging Face Transformers. A small OpenAI-compatible server is built to run the result like any other local model ser...
Source: [Dev.to](https://dev.to/gde/gemma-4-on-a-4-gb-gpu-in-plain-pytorch-from-out-of-memory-to-faster-than-llamacpp-31jb)