Unsloth AI on Twitter / X

Run Gemma 3n locally with our Dynamic GGUFs!✨@Google's Gemma 3n supports audio, vision, video & text and the 4B model fits on 8GB RAM for fast local inference.Fine-tuning is also supported in Unsloth.Gemma-3n-E4B GGUF: https://t.co/PliynxoKQc https://t.co/wMFWLjNaDR pic.twitter.com/lxsMNDmkW8— Unsloth AI (@UnslothAI) June 26, 2025

Gemma 3n: How to Run & Fine-tune | Unsloth Documentation

Run Google's new Gemma 3n locally with Dynamic GGUFs on llama.cpp, Ollama, Open WebUI and fine-tune with Unsloth!

A 10 year old Xeon is all you need - point.free

Or running Gemma 4 on a 2016 Xeon with no GPU, 25 flags, 128 GB of DDR3, and a 25B-parameter MoE.

Gemma 4 and what makes an open model succeed

Hint: it's not benchmark scores.

gemma3n

Gemma 3n models are designed for efficient execution on everyday devices such as laptops, tablets or phones.

Gemma 4 QAT models: Optimizing model compression for mobile and laptop efficiency

We’re releasing Gemma 4 quantization-aware training checkpoints, reducing memory requirements and improving on-device performance.

Gemma 4: Byte for byte, the most capable open models

Gemma 4: our most intelligent open models to date, purpose-built for advanced reasoning and agentic workflows.

MTPLX

2.24x decode TPS increase On Qwen 3.6 27B @ temp 0.6 | Native MTP Speculative Decoding On Apple Silicon With No External Drafter.

drumih/turbo-fieldfare

Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook

INIYSA on Twitter / X

Apple's on-device SLM, while not as strong in simple multilingual tasks as Google's Gemma 3 4B (3.3GB, 5GB in memory), seems to outperform Qwen3 4B or Phi 4 mini reasoning. It's very very impressive, especially considering its extremely reduced size of around ~1.4GB— INIYSA (@lafaiel) June 10, 2025

Google for Developers Blog - News about Web, Mobile, AI and Cloud

Explore Gemma 3 270M, a compact, energy-efficient AI model for task-specific fine-tuning, offering strong instruction-following and production-ready quantization.