Overview
gemma-4-31B-it is an instruction-tuned open-weight model from Google, part of the Gemma 4 family built by Google DeepMind.
- License: Apache 2.0
- Context length: 256K tokens
- Languages: Multilingual
- Minimum hardware: ~19 GB VRAM
- Strengths: reasoning, coding and on-device inference
Highlights
- Document & image understanding — reads PDFs, charts, and handwriting via OCR.
- Reasoning + tool use — a thinking mode plus native structured tool calling.
- 140+ languages — translation, summarization, and drafting across languages.
- 256K context — long-input handling on a single high-VRAM card.
What it is good at
The capability set covers vision, reasoning, code, embeddings, and multilingual text, so a single local model handles a wide span of work:
- Document and image understanding — read PDFs, parse charts, run OCR including handwriting, and answer questions about screenshots or UI captures, with images accepted at varying resolutions.
- Step-by-step reasoning and tool use — a built-in thinking mode works through problems before answering, and native structured tool calling drives agentic workflows on-device.
- Code and multilingual text — write and explain code, and work across more than 140 languages for translation, summarization, and drafting without a network call.
Running it locally
At 32.7B parameters the model is heavy but reachable on a single high-VRAM card. A Q4 quantization sits around 18-19GB and runs comfortably on a 24GB GPU like an RTX 3090 or RTX 4090; Q8 is roughly 32.6GB. The 262,144-token context comes at a cost — a full-length KV cache can add around 22GB on top of the weights, so plan memory around the context length you actually use. On Apple Silicon, unified memory of 32GB or more covers the model plus OS overhead.
huggingface-cli download google/gemma-4-31B-it
From there you can load it through Transformers or serve it with vLLM, or skip the setup and open it in Atomic Chat with one click, which downloads and runs the model offline for you.
License
gemma-4-31B-it is released under the apache-2.0 license. That permits free personal and commercial use with no fee or agreement with Google — you only need to keep the license text in your distribution and note any changes you make, which makes it straightforward to approve for production work.