Install gemma-4-26B-A4B-it Zero Config

The fastest way to get this model running locally is via Docker.

Follow the sequence of steps detailed below.

Then, simply start the container with the provided Docker command.

🛠 Hash code: 140a33dd52d49e07e78605aa33cf4f78 — Last modification: 2026-06-23

CPU: modern architecture (Zen 3 / Alder Lake minimum)
RAM: 32 GB or higher for smooth 32k context lengths
Disk Space: 80 GB NVMe SSD required for fast model weights loading
Graphics: TensorRT-LLM / vLLM inference engine compatible chip

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.