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Gemma-4-31B-IT-NVFP4 No Admin Rights

To install this model locally in the shortest time, opt for a direct curl execution.

Refer to the action plan below to initialize the model.

The installer automatically pulls the model (could be multiple GBs).

The engine benchmarks your hardware to apply the most effective operational mode.

🗂 Hash: 3f16f81ee7cff25e82f3e590d380dc4bLast Updated: 2026-06-29



  • Processor: 6-core 3.5 GHz minimum required
  • RAM: high-speed DDR5 memory preferred for CPU offloading
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

The Gemma-4-31B-IT-NVFP4 model represents a significant advancement in open‑source language models, combining a 31‑billion parameter architecture with instruction‑following capabilities optimized for diverse tasks. Built on the Transformer decoder with grouped‑query attention and rotary positional embeddings, it achieves a balanced trade‑off between computational efficiency and contextual understanding. Through extensive instruction tuning on a curated dataset of textual interactions, the model demonstrates strong performance on reasoning, coding, and conversational prompts while maintaining a compact footprint. A key highlight is its support for NVFP4 quantized weights, which reduces memory usage by up to 75 % without sacrificing accuracy, making it suitable for deployment on edge devices. Benchmark evaluations place it among the top‑tier models in its size class, excelling in both factual retrieval and creative generation tasks. The model is released under an open license, encouraging community contributions and further research into efficient AI systems.

Spec Value
Parameters 31 B
Quantization NVFP4
Architecture Transformer decoder
Attention Grouped‑query + RoPE

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