New Google Gemma 4 12B Claims Near-26B Performance - We Tested Both!
Summary
Google's new Gemma 4 12B model claims near-26B performance. In a local test on RTX 4090, the 26B-A4B model was faster and better but the 12B used less VRAM, making it suitable for laptops.
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Google's new Gemma 4 12B model is designed to run on any laptop with 16GB of RAM
Google releases Gemma 4 12B, a compact AI model optimized for local laptop use with only 16GB of RAM, featuring multi-token prediction and streamlined multimodal capabilities for text, audio, and images.
Ran gemma 4 12b on my 3090 yesterday and I think the local model game just changed
A user reports running Google's Gemma 4 12B model locally on a single RTX 3090 via GGUF quantization, finding strong performance including real 256k context, multimodal capabilities, and function calling that outperforms larger 70B models for coding tasks.
@analogalok: i just ran Google's brand new Unsloth Gemma4 12B dense GGUF on my RTX 4060 using llama.cpp + CUDA 13.2 21 tokens per se…
Google's new Gemma 4 12B is a single decoder-only transformer with encoder-free multimodal input, achieving strong benchmarks while being small enough to run locally on a budget GPU. It is released under Apache 2.0 license.
@KanikaBK: Google just dropped an AI bomb! A BILLION DOLLARS Game is on. Gemma 4 12 B runs on your laptop. 16 GB of RAM, that is a…
Google released Gemma 4 12B, an open-source multimodal AI model under Apache 2.0 that runs locally on laptops with 16GB RAM, targeting enterprise edge deployment.
Introducing Gemma 3
Google introduces Gemma 3, a collection of lightweight open models (1B, 4B, 12B, 27B) designed to run on single GPUs or TPUs, featuring support for 140+ languages, 128k context window, and multimodal capabilities. The models outperform larger competitors like Llama 3 and DeepSeek-V3 while maintaining efficiency for on-device deployment.