Gemma-4-31B-IT-NVFP4 Quantized GGUF

If you need a near-instant local setup, just fetch files via a basic curl request.

Refer to the action plan below to initialize the model.

The download manager will automatically pull several gigabytes of data.

The installer diagnoses your environment to deploy the most compatible profile.

🧩 Hash sum → 936c5c7fae0e9e8972edbfebe5ba140b — Update date: 2026-07-05

Processor: Intel i7 / Ryzen 7 for heavy Quantized models
RAM: 32 GB or higher for smooth 32k context lengths
Disk Space: 80 GB NVMe SSD required for fast model weights loading
Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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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