Google released Gemma 4 12B on June 3, adding a missing middle tier to the Gemma 4 family. The company says the model can run locally on consumer laptops with 16GB of system RAM or VRAM, avoiding the need for expensive AI accelerators.
A middle-ground model for local laptop use
When Google introduced the Gemma 4 lineup in April, it launched four models: E2B and E4B for mobile devices, plus 26B MoE and 31B Dense for server-side deployment. That left a clear gap. The mobile models were lightweight, while the larger versions demanded much stronger hardware, leaving few practical choices for local laptop inference. The new 12B model is meant to sit in that middle position.
The difference is clearer when looking at the model designs. The 26B MoE version uses a mixture-of-experts structure, so each token only activates about 4B parameters during inference. But all 26 billion parameters still need to be loaded into memory to keep routing and inference working efficiently, which keeps memory demand close to a dense model of similar size. The 31B Dense model, by contrast, uses all parameters in every inference pass. Google says Gemma 4 12B uses about 8.1GB of memory in practice, roughly half of the 26B MoE version.
Google says benchmark results are close to 26B MoE
According to Google, Gemma 4 12B performs “nearly as well” as the larger 26B MoE model across several benchmarks. The company highlighted GPQA Diamond, MMLU Pro, and DocVQA, where the published scores come close to the bigger model.
Those figures still need caution. They come from Google’s own evaluation, and a full independent replication has not yet appeared. Benchmark scores are useful, but they do not settle how large or small the gap will be in actual deployments. There is also a practical limit to the “runs on 16GB” claim. The requirement is technically valid, yet on a normal laptop that is also running a browser and office software, available headroom may be tight.
Apache 2.0 license and multimodal support
On licensing, the Gemma 4 family continues to use the Apache 2.0 license. That allows commercial use, modification, and redistribution, giving developers room to build products without case-by-case approval.
Gemma 4 12B is also a multimodal model built on a unified encoder-free architecture. It can handle text, image, audio, and video inputs directly, without attaching separate encoding modules. For developers looking to deploy AI models on local hardware, this release lowers the hardware threshold into a much more common consumer range.