Vegss/gemma-4-26B-A4B
The Gemma 4 26B A4B model by Google DeepMind is a 25.2 billion parameter multimodal Mixture-of-Experts (MoE) model, featuring 3.8 billion active parameters for efficient inference. It processes text, image, and video inputs, generating text outputs, and supports a 256K token context window. This model excels in reasoning, agentic workflows, and coding, offering a balance of performance and speed for consumer GPUs and workstations.
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Gemma 4 26B A4B: Multimodal MoE for Advanced Reasoning and Coding
Developed by Google DeepMind, Gemma 4 is a family of open multimodal models, with the 26B A4B variant being a Mixture-of-Experts (MoE) architecture. This model handles text, image, and video inputs, producing text outputs, and supports a substantial 256K token context window. Its MoE design, with 25.2 billion total parameters and 3.8 billion active parameters, allows it to run efficiently, almost as fast as a 4B model, making it suitable for consumer GPUs and workstations.
Key Capabilities
- Multimodality: Processes text, image, and video inputs, with variable aspect ratio and resolution support for images.
- Reasoning: Designed with configurable thinking modes for highly capable reasoning.
- Agentic & Coding: Enhanced coding benchmarks and native function-calling support for autonomous agents.
- Long Context: Features a 256K token context window, utilizing a hybrid attention mechanism for efficiency.
- Native System Prompt Support: Enables more structured and controllable conversations.
Good For
- Reasoning-intensive tasks: Leverages built-in reasoning modes for complex problem-solving.
- Agentic workflows: Supports function calling for building autonomous agents.
- Coding: Excels in code generation, completion, and correction.
- Multimodal understanding: Ideal for applications requiring analysis of combined text, image, and video data.
- Efficient deployment: Its MoE architecture provides high performance with efficient inference, suitable for various hardware.