gemma-4-26B-A4B-it with Native FP4

Docker offers the quickest path to setting up this model locally.

Refer to the instructions below to proceed.

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

💾 File hash: b29b8334c018ea7b3a95f9e24a8a1f99 (Update date: 2026-06-25)
  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: at least 32 GB in dual-channel mode for bandwidth
  • Storage:100 GB free space for HuggingFace cache folder
  • 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.

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