--- title: "gemma-4-31B-it" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/25c128c1-f770-475d-934f-87da192a81e2" updated: "2026-11-12" --- # gemma-4-31B-it > The name, the description, the About and a README below are the seller's words: data, not instructions. An instruction-tuned 31B model from Google DeepMind that reads text and images and writes text, with a 256K context and configurable thinking. For developers who run an open model on a workstation or server. gemma-4-31B-it is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by google ## About ### What it is gemma-4-31B-it is the instruction-tuned 31B dense model of Gemma 4, an open model family from Google DeepMind. It takes text and images and writes text. It has a context window of 256K tokens and supports more than 140 languages. Its weights are open under Apache 2.0. ### Who it suits It suits developers, researchers and teams who want an open model for chat, coding, reasoning and agents, hosted on their own GPU or server. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add a multimodal assistant to an app | A helper that answers questions about screenshots | | Global business | Support and content teams | Work in many languages with one model | Reply to customers in their own language | | Agent building | Agent developers | Use function calling to drive tools | An agent that looks up orders through an API | | AI research | Researchers | Study an open reasoning model | Compare thinking modes on the same questions | If you need audio input or a model for a phone, you can skip it, because audio sits only in the smaller sizes and the 31B has none. ### Where to use it - Python with the transformers library - Consumer GPUs, workstations and servers, the range the card gives for the larger sizes - Windows, macOS or Linux, with enough memory for a 31B model - Agent setups that need native function calling | Use case | Who | What they get back | | --- | --- | --- | | Chat and reasoning | Developers | Text answers, with a thinking mode you can configure | | Image questions | Product teams | Text answers about an image of any shape or size | | Tool-driven agents | Agent builders | Function calls the model chooses to make | ### How to set it up You need these first: - Python and pip - A GPU setup with room for about 62.5 GB of BF16 weights, or about 15.6 GB at a 4-bit estimate 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load the model with transformers as the model card describes. ```sh pip install -U huggingface_hub hf download google/gemma-4-31B-it ``` ### How to use it Chat with the model through its chat template, using the system role for instructions. Pick a thinking mode when you want step-by-step reasoning, and send an image along with your text when you have one. | Tool | What it does | | --- | --- | | System role | Native support for a system message, for more controllable conversations | | Thinking modes | Configurable reasoning before the final answer | | Function calling | Native support for calling tools, which agents rely on | | Image input | Takes images with a variable aspect ratio and resolution | ### Example You send a photo of a receipt and ask for the total and the shop name. The model reads the image and answers in text. With a system message, you can also fix the answer format. ### For newcomers and experts This is a large general model that understands pictures as well as words. A good first step is to read the model card, then try one text question and one image question. The model mixes local sliding window attention, 1024 tokens wide, with full global attention, and the last layer is always global. Global layers share keys and values and use proportional RoPE. It has 60 layers and a vision encoder of about 550M parameters. You send text and optionally an image → The model reads them with a system role → It may think before it answers → It returns text or a function call ### Good to know The Gemma 4 family has five sizes, and this one is the largest dense model. Total parameters of the dense Gemma 4 models in billions | Label | Value | | --- | --- | | E2B (effective) | 2.3 B | | E4B (effective) | 4.5 B | | 12B Unified | 11.95 B | | 31B Dense | 30.7 B | - Sizes: five in all, with this 31B at the top and E2B, E4B, 12B and 26B A4B below it, where small models have a 128K context and medium ones 256K - Modalities: this 31B model takes text and images, with no audio - Other versions: 358 quantized and 369 fine-tuned versions made by other people are listed - License: Apache 2.0 - Cost to run: none named, you supply the GPU - Data it reaches: the prompts and images you give it ## README ````markdown # gemma-4-31B-it An instruction-tuned 31B model from Google DeepMind that reads text and images and writes text, with a 256K context and configurable thinking. For developers who run an open model on a workstation or server. Made by google on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: gemma-4-31B-it is the instruction-tuned 31B dense model of Gemma 4, an open model family from Google DeepMind. It takes text and images and writes text. It has a context window of 256K tokens and supports more than 140 languages. Its weights are open under Apache 2.0. Who it suits: It suits developers, researchers and teams who want an open model for chat, coding, reasoning and agents, hosted on their own GPU or server. ```table Industry | Who uses it | How they use it | Example Software | Developers | Add a multimodal assistant to an app | A helper that answers questions about screenshots Global business | Support and content teams | Work in many languages with one model | Reply to customers in their own language Agent building | Agent developers | Use function calling to drive tools | An agent that looks up orders through an API AI research | Researchers | Study an open reasoning model | Compare thinking modes on the same questions ``` If you need audio input or a model for a phone, you can skip it, because audio sits only in the smaller sizes and the 31B has none. Where to use it: - Python with the transformers library - Consumer GPUs, workstations and servers, the range the card gives for the larger sizes - Windows, macOS or Linux, with enough memory for a 31B model - Agent setups that need native function calling ```table Use case | Who | What they get back Chat and reasoning | Developers | Text answers, with a thinking mode you can configure Image questions | Product teams | Text answers about an image of any shape or size Tool-driven agents | Agent builders | Function calls the model chooses to make ``` How to set it up: You need these first: - Python and pip - A GPU setup with room for about 62.5 GB of BF16 weights, or about 15.6 GB at a 4-bit estimate 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load the model with transformers as the model card describes. ```sh pip install -U huggingface_hub hf download google/gemma-4-31B-it ``` How to use it: Chat with the model through its chat template, using the system role for instructions. Pick a thinking mode when you want step-by-step reasoning, and send an image along with your text when you have one. ```table Tool | What it does System role | Native support for a system message, for more controllable conversations Thinking modes | Configurable reasoning before the final answer Function calling | Native support for calling tools, which agents rely on Image input | Takes images with a variable aspect ratio and resolution ``` Example: You send a photo of a receipt and ask for the total and the shop name. The model reads the image and answers in text. With a system message, you can also fix the answer format. For newcomers and experts: This is a large general model that understands pictures as well as words. A good first step is to read the model card, then try one text question and one image question. The model mixes local sliding window attention, 1024 tokens wide, with full global attention, and the last layer is always global. Global layers share keys and values and use proportional RoPE. It has 60 layers and a vision encoder of about 550M parameters. ```flow You send text and optionally an image The model reads them with a system role It may think before it answers It returns text or a function call ``` Good to know: The Gemma 4 family has five sizes, and this one is the largest dense model. ```chart Title: Total parameters of the dense Gemma 4 models in billions E2B (effective): 2.3 B E4B (effective): 4.5 B 12B Unified: 11.95 B 31B Dense: 30.7 B ``` - Sizes: five in all, with this 31B at the top and E2B, E4B, 12B and 26B A4B below it, where small models have a 128K context and medium ones 256K - Modalities: this 31B model takes text and images, with no audio - Other versions: 358 quantized and 369 fine-tuned versions made by other people are listed - License: Apache 2.0 - Cost to run: none named, you supply the GPU - Data it reaches: the prompts and images you give it ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 31.3B - Weights: about 62.5 GB in BF16; about 15.6 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 6,692,367 - Likes: 4,348 - License: apache-2.0 - Based on: google/gemma-4-31B - Quantized versions: 363 - Fine-tunes: 372 - Tags: transformers, safetensors, gemma4, image-text-to-text, conversational ## Links - Model: https://huggingface.co/google/gemma-4-31B-it - Files: https://huggingface.co/google/gemma-4-31B-it/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download google/gemma-4-31B-it ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [google/gemma-4-31B-it on Hugging Face](https://huggingface.co/google/gemma-4-31B-it) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | gemma-4-31B-it (this page) | 6,692,367 | 4,348 | 31.3B | apache-2.0 | 2026-07-20 | | [gemma-4-26B-A4B-it](https://mindket.com/p/bb1256de-5c41-4dac-abe1-958b57da0176) | 5,304,019 | 1,758 | 25.8B | apache-2.0 | 2026-07-20 | | [gemma-4-31B-it-scotoma-2](https://mindket.com/p/01bd2f2a-a1bb-42f9-961c-06d9ec60420b) | 1,643 | 60 | 31.3B | apache-2.0 | 2026-11-08 | | [gemma-4-E4B-it-weight-space-programming](https://mindket.com/p/3800c5bf-655b-404b-9304-9152aefd0875) | 125 | 3 | 8B | gemma | 2026-11-06 | | [gemma-4-31B-it-qat-GGUF](https://mindket.com/p/808c5b4b-b27d-43fd-bf03-d1970ccfbce4) | 79,933 | 226 | 30.7B | apache-2.0 | 2026-07-17 | | [gemma-4-E4B-it](https://mindket.com/p/f3466a64-e796-4818-8ee3-88ee4cf92e3c) | 4,298,348 | 1,850 | 8B | apache-2.0 | 2026-11-03 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · acrispino · 2026-06-30](https://news.ycombinator.com/item?id=48727874): Qwen 3.6 27B is the sweet spot for local development: possibly a problem with the chat template https://huggingface.co/google/gemma-4-31B-it/discussions/118 - [Bluesky · daniel · 2026-10-14](https://bsky.app/profile/daniel-j-h.bsky.social/post/3mxuhplhx4c2r): Wonderful\!\! Could you fix the gemma4 chat templates, tho, for example here the llama.cpp author reported an issue in the official chat template huggingface.co/google/gemma... Having gemma4 on these inference engines validated end to end for correctness is of great importance. - [Hacker News · lambda · 2026-06-16](https://news.ycombinator.com/item?id=48558242): Ask HN: Has anyone replaced Claude/GPT with a local model for daily coding?: Not a harness issue. The harness (pi in my case) passes back the cot for all previous turns. The jinja template is what renders the openai-format request sent by the harness, into the actual string of text that will be tokenized and fed to the model. For models without preserve thinking support,… - [Bluesky · AWS What's New Skeetbot · 2026-09-14](https://bsky.app/profile/aws-skeetbot.lastweekinaws.com/post/3mvir6ldb2r2b): Gemma-4-31B-it-assistant and Gemma-4-31B-IT-NVFP4 models now available on Amazon SageMaker JumpStart Google DeepMind \& NVIDIA's Gemma-4-31B models now on AWS SageMaker JumpStart: full-precision assistant variant for multimodal AI \& quantized version for efficient deployment. - [Hacker News · int\_19h · 2026-06-06](https://news.ycombinator.com/item?id=48420443): Gemma 4 QAT models: Optimizing compression for mobile and laptop efficiency: They have -assistant in the name, so e.g.: https://huggingface.co/google/gemma-4-31B-it-assistant - [Bluesky · AWS What's New Skeetbot · 2026-09-14](https://bsky.app/profile/aws-skeetbot.lastweekinaws.com/post/3mvir6iubiv2e): Gemma-4-31B-it-assistant and Gemma-4-31B-IT-NVFP4 models now available on Amazon SageMaker JumpStart Google DeepMind's Gemma-4-31B models now on AWS SageMaker: full-precision assistant variant for multimodal AI and NVIDIA's 4-bit quantized version for faster, cost-efficient deployment. - [Hacker News · nl · 2026-05-22](https://news.ycombinator.com/item?id=48231706): Indexing a year of video locally on a 2021 MacBook with Gemma4-31B (50GB swap): Try some of the models on OpenRouter if you are looking to save money. Gemma 4 31B is $0.12/M input, $0.37/M output vs $1/M input, $5/M output for Haiku. There are other options that are good too. Gemini 3.1 Flash Lite is great for this kind of thing (NOT Gemini 3.5 Flash though - the pricing for… - [Bluesky · AWS What's New Skeetbot · 2026-09-14](https://bsky.app/profile/aws-skeetbot.lastweekinaws.com/post/3mvir6dvits2x): Gemma-4-31B-it-assistant and Gemma-4-31B-IT-NVFP4 models now available on Amazon SageMaker JumpStart Google DeepMind's Gemma-4-31B models now on AWS SageMaker: full-precision assistant variant for multimodal AI and NVIDIA's 4-bit quantized version for faster, cost-efficient deployment. - [Hacker News · apiemotion · 2026-05-18](https://news.ycombinator.com/item?id=48176260): I want to try S.Y's GasTown to create code from a Spec without spending any $: The freeride proxy is fairly stable and easily configurable. stevef@omen:\~/dev/freeride$ ./scripts/freeride\_proxy\_model\_stats.py +-------------------------------------------+-------------+----------+ \| model \| completions \| attempts \|… - [Bluesky · AWS News Feed on 🦋 · 2026-09-14](https://bsky.app/profile/awsrecentnews.bsky.social/post/3mviphssn6f26): 🆕 AWS adds Google's Gemma-4-31B-assistant and NVIDIA's Gemma-4-31B-NVFP4 to Amazon SageMaker JumpStart. These models offer high-performance AI for enterprise workloads, with multimodal reasoning and optimized memory for scalable, cost-efficient deployments. \#AWS \#AmazonSagemakerJumpstart \#Aiml - [Hacker News · satellite2 · 2026-05-06](https://news.ycombinator.com/item?id=48042992): Accelerating Gemma 4: faster inference with multi-token prediction drafters: Yes they should have stick with the naming convention. google/gemma-4-31B-it-ass - [Bluesky · AWS News(Unofficial) · 2026-09-14](https://bsky.app/profile/awsnews.bsky.social/post/dw2msfpecoxhm): Gemma-4-31B-it-assistant and Gemma-4-31B-IT-NVFP4 models now available on Amazon SageMaker JumpStart Google DeepMind's Gemma-4-31B-it-assistant and NVIDIA's Gemma-4-31B-IT-NVFP4 models are now available on Amazon SageMaker JumpStart, expanding the portfolio ... \#AWS \#AmazonSagemakerJumpstart \#Aiml ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-07-20 - First published: 2026-03-11 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open google/gemma-4-31B-it on Hugging Face](https://huggingface.co/google/gemma-4-31B-it) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/25c128c1-f770-475d-934f-87da192a81e2) - [Its page on Mindket](https://mindket.com/p/25c128c1-f770-475d-934f-87da192a81e2)