Google DeepMind launched Gemma 4 this week, releasing four open-weight models that fit entirely on a single 80GB Nvidia H100 GPU while delivering benchmark scores that rival models 20 times their size. The release marks Google's most aggressive move yet against Meta's Llama in the open model race and hands Nvidia a new reason to sell GPUs to enterprises that want to run AI locally rather than pay per-token cloud fees.
The model family spans four sizes. At the top sits a 31-billion-parameter dense transformer that currently ranks third among all open models on the Arena AI text leaderboard with an estimated score of 1452. A 26-billion-parameter mixture-of-experts variant activates only 3.8 billion parameters during inference and secured the sixth spot on the same leaderboard. Two smaller "effective" models at 4 billion and 2 billion parameters target smartphones, Raspberry Pi boards and Nvidia Jetson Orin Nano edge devices where battery life and memory constraints dominate design decisions.
The most consequential change in this release has nothing to do with model architecture. Google shipped Gemma 4 under an Apache 2.0 license, abandoning the restrictive custom license that governed previous Gemma generations. That shift removes commercial use restrictions and acceptable-use policy enforcement that previously forced enterprise legal teams to review every deployment. For organizations building sovereign AI systems on-premises, the licensing clarity matters as much as the benchmark numbers.
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