gemma-4-26B-A4B-it Locally via Ollama 2 with Native FP4

Using Docker is the absolute quickest way to install this model on your local machine.

Follow the step-by-step instructions below.

After that, launch the environment using docker-compose.

🧩 Hash sum → 4a5bf8a45ca4775436522537cbba57f9 — Update date: 2026-06-25

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Processor: high single-core performance needed for token latency
RAM: required: 16 GB absolute minimum for small models
Disk Space: 100 GB for multi-modal model vision components
GPU: high memory bandwidth GPU for next-gen local AI pipeline

The gemma-4-26B-A4B-it model represents a significant advancement in open‑source language models, combining a massive 26‑billion parameter architecture with optimized inference performance. It leverages an attention‑sparse design that reduces computational load while maintaining high fidelity in both factual and creative tasks. The model supports a 2048‑token context window and incorporates a refined instruction‑tuning pipeline that improves alignment with user intent. A comparison with peer models shows superior scores in reasoning, code generation, and multilingual understanding, as summarized below.

Metric	Value
Parameters	26 B
Context Length	2048 tokens
Training Data	Web‑scale multilingual corpus
Inference Speed	~120 tokens/s on GPU

Users can integrate the model into production environments via standard APIs, benefiting from its balanced trade‑off between size, speed, and capability.

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gemma-4-26B-A4B-it Locally via Ollama 2 with Native FP4

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