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LatestGoogle DeepMind4
Google DeepMindAny-to-Any

Google Releases Gemma 4, a 26B Vision-Language Model

The new open-source model from DeepMind uses a Mixture-of-Experts architecture to handle both text and image inputs efficiently.

Mar 11, 2026
Major releaseApache 2.0
Gemma 4

Google DeepMind has expanded its open-source offerings with the release of Gemma 4 26B Instruct, a new vision-language model. Published under a permissive Apache 2.0 license, this model is designed to understand and process both text and images, making it a versatile tool for multimodal applications.

An Efficient Multimodal Architecture

The key innovation in Gemma 4 is its Mixture-of-Experts (MoE) architecture. While the model contains a total of 26 billion parameters, it's designed for efficiency by activating only a fraction of them for any given task. The model's designation, "A4B," suggests that approximately 4 billion parameters are active at a time, offering potent performance without the full computational cost of a dense 26B model.

As an instruction-tuned model, Gemma 4 26B is optimized to follow user prompts and commands, making it suitable for a wide range of chat and assistant-style applications. Researchers and developers can access the model and its technical details on its Hugging Face repository.

This release signals Google's continued investment in the open-source AI ecosystem, providing a powerful, state-of-the-art multimodal model to the community. The efficient MoE design makes advanced vision-language capabilities more accessible, enabling new possibilities for applications that can see and reason about the world.

Sources

  • google/gemma-4-26B-A4B-it

    Hugging Face

    Visit
OlderGoogle Releases Multimodal Gemma 4 31B ModelGoogle DeepMind · Any-to-Any · 8 months agoNewerNeedle: A 26M-Param Model Built for On-Device Tool CallsCactus Compute · Code · 8 months ago

Get the model

Hugging Face

Specs

Parameters26B · MoE
Context window262K tokens
Size51.6 GB
PrecisionBF16
ArchitectureGemma4ForConditionalGeneration
LicenseAPACHE-2.0
Downloads1.7M
Likes1.7K

Can you run it?

Runs on a 24 GB GPU at 4-bit.

  • BF16 (as published)

    80 GB GPU (A100 / H100) · 128 GB Mac Studio

    54.6 GB
  • 8-bit

    32 GB GPU (RTX 5090) · 48 GB Mac

    30.4 GB
  • 4-bit

    24 GB GPU (RTX 3090 / 4090) · 32 GB Mac

    18.5 GB
Your machine
GGUF builds
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLMVision-Language
6 versions — view changelog

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