Google releases Gemma 4 12B open-source model, can be run locally on a 16GB consumer laptop

Google Releases Gemma 4 Family Gap: A 12B Parameter New Model Runs Locally on Consumer Laptops with Just 16GB Memory, Official Benchmarks Near Twice the Size 26B MoE Version.
(Background: Google Launches New AI Application Dreambeans! Turn Your Daily Life into Limited Edition "Cartoon Stories")
(Additional Context: Google Invests Heavily in AI! Alphabet Expands Equity Financing to $85 Billion, Secures $10 Billion Investment from Berkshire Hathaway)

On June 3, Google announced the release of Gemma 4 12B, a model that requires no expensive AI accelerators costing tens of thousands of dollars, just a computer with 16GB of system memory (RAM) or graphics card memory (VRAM) to run locally.

The Gap in the Gemma 4 Family

In April this year, Google launched four models in the Gemma 4 family: the mobile-optimized E2B and E4B, and the server-oriented 26B MoE and 31B Dense. This product line covers lightweight edge devices to heavy cloud servers, but there’s a clear gap in the middle. The mobile versions are too lightweight, and models above 26B require substantial hardware specs, leaving almost no options for local laptop scenarios.

The 12B model was created precisely to fill this gap.

To clarify, the 26B MoE is a "Mixture of Experts" model, where MoE means the model calls upon specific expert neurons as needed. This means not all parameters are activated during each inference. Simply put, this architecture allows the model to activate only a subset of neurons during computation; the 26B version uses about 4B parameters per token. However, the cost is that all 26 billion parameters must be pre-loaded into memory to maintain routing and inference speed, resulting in memory usage close to that of a similarly sized dense model.

The 31B Dense model is a "dense" architecture, using all parameters for each inference, with no savings. Every response is generated with full effort. In comparison, the actual memory usage of Gemma 4 12B is about 8.1GB, roughly half of the 26B MoE.

Meanwhile, the Gemma 4 family continues to use the Apache 2.0 license adopted this year, an open license that allows commercial use, modification, and redistribution. Developers can directly deploy it in their products without applying for individual permissions.

"Almost as Powerful"

Google claims that Gemma 4 12B performs "almost as strongly" as the twice-sized 26B MoE across multiple benchmarks, enough to rival models with twice the parameters. The official benchmarks include GPQA Diamond (graduate-level scientific reasoning), MMLU Pro (multi-domain knowledge), DocVQA (document visual question answering), among others, with figures approaching those of the 26B version.

However, several points warrant cautious interpretation.

First, these are Google’s self-reported official scores, not independently verified by third parties. Benchmarks are starting points, not endpoints; real-world application differences may be larger or smaller than the scores suggest. Second, "16GB can run" is technically true, but actual memory usage tests show about 8.1GB consumed. For a typical laptop running a browser and document software simultaneously, remaining memory is tight, so not everyone can run it smoothly.

Gemma 4 12B is also a multimodal model, using a unified architecture without encoders, meaning the same model can directly process text, images, audio, and video inputs without needing separate encoding components.