gemma-4-31B-it

gemma-4-31B-it

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

Follow the straightforward walkthrough provided below.

An automated background process downloads all required large-scale files.

The configuration wizard runs silently to set up the model for peak performance.

🔐 Hash sum: 9c2218b5220d0bafcd909c25d8fe632e | 📅 Last update: 2026-06-27



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: at least 32 GB in dual-channel mode for bandwidth
  • Storage:100 GB free space for HuggingFace cache folder
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

The Gemma-4-31B-it model represents a significant advancement in open‑source language models, combining a 31 billion parameter architecture with sophisticated instruction tuning. It leverages a mixture‑of‑experts design to achieve both high performance and computational efficiency, making it suitable for a wide range of commercial and research applications. The model supports multimodal inputs, allowing users to process text, images, and audio within a unified framework. Benchmark evaluations place it among the top‑tier models in reasoning, coding, and factual knowledge tasks, often matching or surpassing proprietary alternatives. An accompanying

provides detailed technical specifications and a comparative performance snapshot against earlier Gemma releases.

Specification Value
Parameters 31 B
Context Length 8 K tokens
Training Data Web‑scale multilingual corpus
Inference Speed ~120 MFLOPS
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