Models•4 min read•Hugging Face Blog

Welcome Gemma 4: Frontier multimodal intelligence on device

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Gemma 4 31B achieves a score of 1,452 points in the LMArena ranking, matching the performance of powerful competitor models despite having nearly thirty times fewer parameters. The new family of multimodal models from Google DeepMind, released on April 2, 2026, under the Apache 2 license, redefines the concept of on-device performance. Thanks to the Mixture-of-Experts (MoE) architecture in the 26B variant, the system activates only 4 billion parameters while offering full support for image, video, and audio analysis with a context window of up to 256,000 tokens. A key innovation is the implementation of Per-Layer Embeddings (PLE) and shared KV Cache memory, which drastically reduces resource requirements while maintaining high precision. For users and developers, this represents a breakthrough in the design of local AI agents – smaller variants (2.3B and 4.5B) effortlessly handle audio processing and variable-aspect-ratio images directly on laptops or smartphones. Full integration with the Hugging Face ecosystem, llama.cpp, MLX, and WebGPU libraries ensures that advanced multimodal intelligence no longer requires costly cloud infrastructure. Gemma 4 thus becomes the foundation for a new generation of responsive, private applications that understand the visual and auditory world in real-time, operating entirely within the user's local environment.

Four sizes, infinite possibilities

Google has decided to diversify its offering by introducing four variants of the model, each addressing different market needs. The key differentiator is the division into dense models and those based on the **Mixture-of-Experts (MoE)** architecture. All versions are available in both a base variant and an instruction-tuned (IT) variant.
  • Gemma 4 E2B: A model with an effective 2.3B parameters (5.1B with embeddings), offering a 128k context window. It supports text, image, and audio.
  • Gemma 4 E4B: A 4.5B parameter version (8B with embeddings), also with a 128k window and full multimodal support (including audio).
  • Gemma 4 31B: A powerful dense model with a 256k context window, designed for the most demanding analytical tasks.
  • Gemma 4 26B A4B: An MoE architecture, where only 4B out of a total of 26B parameters are active. It offers a 256k window and performance comparable to the largest dense units.

A New Standard for Local Artificial Intelligence

The introduction of **Gemma 4** is a clear signal that the boundary between cloud models and those running locally is blurring faster than anticipated. Through the use of **Dual RoPE** (standard for local layers and proportional for global ones) and intelligent attention management, these models handle massive context windows while remaining responsive on consumer hardware. The ability to run a "frontier" class performance model on a laptop or smartphone, while simultaneously supporting image and audio, opens a new era in the development of AI agents. **Gemma 4** is not just another iteration; it is a mature ecosystem that, thanks to broad compatibility with libraries like **Unsloth Studio**, **TRL**, and **Vertex AI**, will become the foundation for a new wave of creative and business AI applications. Google DeepMind has proven that the future of artificial intelligence lies not only in massive computing clusters but in intelligent, optimized architecture available to every developer.

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