What is gemma_pytorch?

google/gemma_pytorch — explained in plain English

Analysis updated 2026-06-26

★ 5,674PythonAudience · developerComplexity · 4/5Setup · hard

Mindmap

mindmap
  root((gemma_pytorch))
    What it does
      Run Gemma locally
      Text and image input
      No API cost
    Model sizes
      1B to 27B params
      Base and instruct
      Int8 quantized
    Hardware targets
      CPU
      GPU CUDA
      Google TPU
    Setup
      Docker containers
      Kaggle checkpoints
      Free Colab notebook

mindmap root((gemma_pytorch)) What it does Run Gemma locally Text and image input No API cost Model sizes 1B to 27B params Base and instruct Int8 quantized Hardware targets CPU GPU CUDA Google TPU Setup Docker containers Kaggle checkpoints Free Colab notebook

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What do people build with it?

USE CASE 1

Run Gemma AI models on your own GPU or CPU without paying for API access

USE CASE 2

Build text generation or question-answering tools using Google's open-weight models

USE CASE 3

Experiment with multimodal input by feeding both text and images to a Gemma model

USE CASE 4

Reduce memory usage on smaller machines by loading the int8 quantized model variant

What is it built with?

PythonPyTorchDockerCUDATPUHugging Face

How does it compare?

	google/gemma_pytorch	bytedance/latentsync	agiresearch/aios
Stars	5,674	5,675	5,676
Language	Python	Python	Python
Setup difficulty	hard	hard	hard
Complexity	4/5	4/5	5/5
Audience	developer	researcher	researcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1h+

Requires downloading large model checkpoint files from Kaggle or Hugging Face and setting up Docker with GPU drivers before running inference.

Gemma model weights use Google's own Gemma license, check the specific model page on Kaggle or Hugging Face for commercial use terms.

So what is it?

This repository contains Google's official PyTorch code for running Gemma, a family of AI language models that Google built using the same research behind its larger Gemini models. Gemma models are released with open weights, meaning anyone can download and run them without paying for API access. Gemma comes in several sizes. The smallest version has 1 billion parameters, while the largest has 27 billion. Smaller models run faster and need less hardware, larger ones tend to give better answers. Some variants handle only text, while others are multimodal, meaning they can accept both text and images as input. There are also instruction-tuned variants that are set up to follow conversational prompts out of the box. To run the models, you download a checkpoint file from Kaggle or Hugging Face and then run inference scripts provided in this repo. The setup uses Docker containers to manage dependencies, which means you package everything into an isolated environment before running. Inference can run on a regular CPU, a consumer or professional GPU, or on Google's own TPU hardware. An int8 quantized option is available for reducing memory usage on smaller machines. The repo supports both standard PyTorch and a variant called PyTorch/XLA, which is designed to run efficiently on TPUs. Separate Docker files and run scripts are provided for each hardware target, so you pick the one that matches your setup. If you want to try Gemma without installing anything, Google provides a free Colab notebook linked in the README. This repository is an unofficial reference implementation rather than a supported Google product.

Copy-paste prompts

Prompt 1

Write a Python script using google/gemma_pytorch to load the Gemma 2B checkpoint and generate a response to: 'Explain black holes in simple terms'

Prompt 2

Show me the Docker command to run inference with the Gemma 7B instruction-tuned model on a GPU using the scripts in this repo

Prompt 3

How do I run the Gemma model on a Google TPU using the PyTorch/XLA variant in gemma_pytorch?

Prompt 4

Create a Python example that loads the int8 quantized Gemma model to reduce VRAM usage below 8GB

Prompt 5

What is the difference between Gemma base and instruction-tuned variants and which should I use for a chatbot?

Frequently asked questions