Gemma 4: The 128K Multimodal Powerhouse in Your Terminal
Google has released the Gemma 4 family of models, which includes a range of sizes from 2B to 31B parameters. The models feature native multimodal vision support and a significant 128K context window, but developers must be cautious of VRAM limitations. A hands-on guide is provided for setting up local multimodal inference using the models.
- ▪Gemma 4 is available in three variants: 2B, 9B, and 31B, catering to different hardware capabilities.
- ▪The 128K context window can lead to increased memory consumption, complicating local deployment.
- ▪Developers can run Gemma 4 locally using Python and Hugging Face's transformers library without needing cloud infrastructure.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3936254) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ajay Mourya Posted on May 25 Gemma 4: The 128K Multimodal Powerhouse in Your Terminal #devchallenge #gemmachallenge #gemma Gemma 4 Challenge: Write about Gemma 4 Submission A raw, developer-first look at Google’s new open-weight Gemma 4 family—featuring a hands-on local Python setup, a comparison of the 2B, 9B, and 31B variants, and the brutal math of the 128K context window VRAM consumption. The Local AI Hype vs. The VRAM Reality Every major AI release follows the same cycle.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
