# Gemma Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/gemma
> Markdown URL: https://aitinkerers.org/technologies/gemma.md
> Technology record last updated: 2026-02-26T08:14:41Z
> Generated: 2026-09-21T10:49:54Z

Gemma is Google DeepMind's family of lightweight, state-of-the-art open models, built on the same core technology as the Gemini series.

Gemma is Google DeepMind's open-weight model family (LLMs and VLMs), leveraging the same proprietary research and technology as the high-performance Gemini series. The models are released in various parameter sizes, from 270M up to 27B, and deliver state-of-the-art performance for their scale (e.g., Gemma 3 27B). Developers use Gemma to build and customize generative AI applications, supporting diverse use cases like on-device deployment (Gemma 3n) and specialized tasks (CodeGemma, PaliGemma 2). This release promotes responsible AI innovation and community-driven development.

- Official technology site: https://ai.google.dev/gemma
- Public AI Tinkerers demos and talks: 4
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Teaching an LLM to Play Riftbound](https://ottawa.aitinkerers.org/talks/rsvp_CPcwK2QKRgg)

Samuel demonstrates a framework he built to teach AI agents to play Riftbound, the League of Legends-based trading card game. The system translates English card text and the current board state into legal actions, uses Gemma to choose moves in player-versus-bot or bot-versus-bot simulations, and is designed to generate training experience so a faster machine-learning policy can eventually select optimal plays without relying on slower LLM inference at the final decision stage.

- Event context: AI Tinkerers Ottawa Meetup - June 20th, 2026 — 2026-06-20 — Ottawa
- Public talk page: https://ottawa.aitinkerers.org/talks/rsvp_CPcwK2QKRgg

### [Self-hosting LLMs on Google Cloud with GPUs using vLLM](https://amsterdam.aitinkerers.org/talks/rsvp_PacH4objgRg)

Don't want to be depedent on paid APIs for using LLMs? Then self-hosting is the way to go. However, due to the "large" aspect of LLMs it's extremely difficult to self-host them. Everybody talks about self-hosting LLMs but no one actually does it. vLLM is a framework that optimises the hosting of such LLMs 👉 https://github.com/vllm-project/vllm I made a simple tutorial on how to deploy your own GEMMA 2 model on Google Cloud 👉 https://github.com/ThomasVrancken/info9023-mlops/tree/main/demos/04_vllm

- Event context: AI Tinkerers Amsterdam - April Edition — 2025-04-02 — Amsterdam
- Public talk page: https://amsterdam.aitinkerers.org/talks/rsvp_PacH4objgRg

### [Generating "stories" from your digital trace data](https://paris.aitinkerers.org/talks/rsvp_67DnuLeXU-k)

I've been working on an open source project that leverages gen ai to make digital trace data come alive. Digital trace data consists of a person's Google search history, WhatsApp messages and ChatGPT conversations and tells the true story of a person's identity, free of the filters we self impose when sharing content on social apps. In its current form, the data pipeline analyzes the Google Takeout's "Activity.json" file and generates sequences of captioned images that showcase the evolution through time of one's life experiences inferred from it (personal growth, curiosities and worries, dreams and aspirations, etc). I will demo the first working iteration of the app (slightly different from the description above) and a sneak peek of the second iteration. Will also showcase the underlying data pipeline and how transformer models are being deployed.

- Event context: AI Tinkerers - Paris Meetup on October 15th — 2024-10-15 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_67DnuLeXU-k

### [Run Local, open source AI](https://singapore.aitinkerers.org/talks/rsvp_xdg42kFlb9w)

We're AI tinkerers passionate about local, open source AI. We'll demo how you can run popular open source AI models like Llama3, Mistral and Gemma on your own laptop with Jan.ai and Cortex.so.

- Event context: AI Tinkerers Singapore: Inaugural Meetup - August 2nd, 2024 — 2024-08-02 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_xdg42kFlb9w

## Related Technologies

- [vLLM](https://aitinkerers.org/technologies/vllm) ([Markdown](https://aitinkerers.org/technologies/vllm.md)) — 33 public demos
- [AI](https://aitinkerers.org/technologies/ai) ([Markdown](https://aitinkerers.org/technologies/ai.md)) — 55 public demos
- [Cortex](https://aitinkerers.org/technologies/cortex) ([Markdown](https://aitinkerers.org/technologies/cortex.md)) — 2 public demos
- [Flux](https://aitinkerers.org/technologies/flux) ([Markdown](https://aitinkerers.org/technologies/flux.md)) — 8 public demos
- [game-playing agents](https://aitinkerers.org/technologies/game-playing-agents) ([Markdown](https://aitinkerers.org/technologies/game-playing-agents.md)) — 1 public demo
- [Google Cloud](https://aitinkerers.org/technologies/google-cloud) ([Markdown](https://aitinkerers.org/technologies/google-cloud.md)) — 8 public demos
- [Jan](https://aitinkerers.org/technologies/jan) ([Markdown](https://aitinkerers.org/technologies/jan.md)) — 2 public demos
- [Kubernetes](https://aitinkerers.org/technologies/kubernetes) ([Markdown](https://aitinkerers.org/technologies/kubernetes.md)) — 35 public demos
- [Large Language Models](https://aitinkerers.org/technologies/large-language-models) ([Markdown](https://aitinkerers.org/technologies/large-language-models.md)) — 8 public demos
- [llama](https://aitinkerers.org/technologies/llama) ([Markdown](https://aitinkerers.org/technologies/llama.md)) — 48 public demos
- [Llama 3](https://aitinkerers.org/technologies/llama-3) ([Markdown](https://aitinkerers.org/technologies/llama-3.md)) — 38 public demos
- [LLM](https://aitinkerers.org/technologies/llm) ([Markdown](https://aitinkerers.org/technologies/llm.md)) — 123 public demos
- [Machine Learning](https://aitinkerers.org/technologies/machine-learning) ([Markdown](https://aitinkerers.org/technologies/machine-learning.md)) — 20 public demos
- [Mistral](https://aitinkerers.org/technologies/mistral) ([Markdown](https://aitinkerers.org/technologies/mistral.md)) — 24 public demos
- [ML](https://aitinkerers.org/technologies/ml) ([Markdown](https://aitinkerers.org/technologies/ml.md)) — 11 public demos
- [Ray](https://aitinkerers.org/technologies/ray) ([Markdown](https://aitinkerers.org/technologies/ray.md)) — 6 public demos
- [Reinforcement Learning](https://aitinkerers.org/technologies/reinforcement-learning) ([Markdown](https://aitinkerers.org/technologies/reinforcement-learning.md)) — 6 public demos