gemma-4-E4B-it-GGUF Offline on PC with Native FP4

The most rapid route to a local installation of this model is through WSL2.

Kindly follow the on-screen instructions below.

The download manager will automatically pull several gigabytes of data.

The setup file includes a feature that instantly optimizes all configurations.

🔗 SHA sum: cacd58329603a04ffc79edc90dad8f56 | Updated: 2026-06-30
  • Processor: next-gen chip for heavy context processing
  • RAM: enough space for background apps and OS overhead
  • Storage: extra room for future model updates and datasets
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

The gemma-4-E4B-it-GGUF model represents a significant advancement in open‑source language models, combining efficient inference with strong reasoning capabilities. Built on the Gemma architecture, it leverages a 4‑billion parameter configuration that balances speed and accuracy for a wide range of tasks. Its context window extends to 8K tokens, enabling the model to understand longer prompts and maintain coherence across complex dialogues. In benchmark evaluations, the model achieves state‑of‑the‑art performance on reasoning, coding, and multilingual tasks while consuming minimal GPU resources. The accompanying GGUF quantization format ensures seamless integration with popular inference frameworks, reducing memory footprint and accelerating deployment. Developers and researchers can fine‑tune the model for specialized applications, benefiting from its robust tokenization and extensive community support.

Parameters 4 B
Context length 8K tokens
Quantization GGUF (Q4_K_M)
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