Google DeepMind Launches Gemma 4 Open Models Under Apache 2.0

What happened

Google DeepMind used X to introduce Gemma 4 as a new family of open models that can run on local hardware under an Apache 2.0 license. In a Google Developers Blog post published on April 2, 2026, Google positioned Gemma 4 as a toolkit for on-device AI rather than a model family meant only for cloud benchmarking. The company says Gemma 4 supports multi-step planning, autonomous action, offline code generation, audio-visual processing, and more than 140 languages.

The more important part of the launch is the delivery stack around the models. Google paired the announcement with Android AICore access, Google AI Edge Gallery, and LiteRT-LM. That makes Gemma 4 less of a standalone weights drop and more of a ready-to-test platform for developers building mobile, desktop, web, and edge applications.

Why it matters

Open-weight model releases are no longer judged only by whether the weights are downloadable. Developers increasingly care about how quickly those models can be embedded into real products. Google is clearly targeting that shift. The company highlighted Agent Skills in AI Edge Gallery as an example of multi-step workflows running entirely on-device, including querying knowledge sources, producing structured summaries, and connecting Gemma 4 to other models.

Gemma 4 is licensed under Apache 2.0, which lowers friction for commercial experimentation.

Google says LiteRT-LM adds constrained decoding, dynamic context handling, and low-memory operation for broader deployment.

The company is explicitly tying Gemma 4 to phones, desktops, web runtimes, Raspberry Pi, and other edge hardware rather than only to hosted inference.

That is strategically important because it widens the meaning of open-model competition. It is no longer only about leaderboard rankings or raw token throughput. It is also about packaging, tooling, and whether developers can move from model announcement to product prototype with minimal setup. Google is trying to close that gap by shipping the model family alongside concrete developer surfaces.

For the broader market, Gemma 4 is a reminder that agentic AI is not staying in the data center. If model quality, memory footprint, and local tooling keep improving together, more agent-style experiences will move onto user devices and into mixed offline-online workflows. That has implications for latency, privacy, cost, and platform control. Original source: Google Developers Blog.