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gemma-4-26B-A4B-it Locally via Ollama 2

gemma-4-26B-A4B-it Locally via Ollama 2

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

Use the instructions provided below to complete the setup.

Next, start the model by running the docker-compose command.

🔒 Hash checksum: 05203199f55cde7fe0997841d1443060 • 📆 Last updated: 2026-06-26
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  • Processor: high single-core performance needed for token latency
  • RAM: high-speed DDR5 memory preferred for CPU offloading
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

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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