--- title: "gemma-4-31B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c35b88b1-d4f0-4377-b018-ecdd3a303439" updated: "2026-11-12" --- # gemma-4-31B > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads text and images and writes text, for developers and researchers who want Google DeepMind's largest dense open-weights Gemma 4 model for coding, reasoning and image questions. gemma-4-31B 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 is the largest dense size in the open Gemma 4 family from Google DeepMind. It reads text and images and writes text. The repository holds about 31.3B parameters as Safetensors files for the transformers library, and it has no audio input. ### Who it suits It suits developers and researchers who want an open-weights model for text generation, coding, reasoning and image understanding, run on a consumer GPU or a workstation. Who uses this model | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add coding help and reasoning to their own apps | Ask it to review a function | | Research | Machine learning researchers | Study a dense open-weights model | Compare it with the smaller sizes in the family | | Product teams | Agent builders | Build assistants that call functions | Give the model a tool and let it decide when to use it | Anyone who needs audio input can skip this size, since the card lists audio only for the E2B, E4B and 12B models. ### Where to use it - Python scripts that use the transformers library - A consumer GPU or a workstation - Windows, macOS or Linux with Python, for downloading the files Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Text generation | Writers and developers | Written replies to a prompt | | Coding help | Developers | Code and explanations in text | | Image questions | Anyone with pictures to check | Text answers about an image you supply | | Reasoning | Researchers | Step-by-step answers, with thinking modes you can configure | ### How to set it up You need Python and plenty of disk space. The BF16 files weigh about 62.5 GB, and a 4-bit copy is estimated at about 15.6 GB. - Python with pip - The huggingface\_hub package - Free disk space for the weights 1. Install huggingface\_hub. 2. Run the download command to save the model files on your computer. 3. Load the files with the transformers library in your own script. ```bash pip install -U huggingface_hub hf download google/gemma-4-31B ``` ### How to use it Load the downloaded files with transformers and give the model text, or an image together with text. The system role lets you set ground rules for a conversation. The card says thinking modes are configurable and points to its own pages for the exact settings. ### Example You hand the model a screenshot of an error message and ask what it means in plain words. It answers in text. A system message can tell it to keep replies short and formal. ### For newcomers and experts Think of it as a model that looks at a picture and a question, then writes an answer. A first small test is one image and one short question. The family also has smaller sizes if this one is too big for your computer. The dense design means all parameters work on every token. The card gives 60 layers, a sliding window of 1024 tokens, a 256K-token context length and a vocabulary of 262K. Attention alternates between local windows and full global layers, with the last layer always global. Global layers share keys and values and use Proportional RoPE to save memory. The vision encoder has roughly 550M parameters. Text or image goes in → Vision encoder reads the image → Hybrid attention layers → Text comes out ### Good to know The family reaches a context window of 256K tokens and supports more than 140 languages. Other people have published 44 quantized and 103 fine-tuned versions of this model. The release has pre-trained and instruction-tuned variants, so check the repository name to see which one you get. Estimated weight size | Label | Value | | --- | --- | | BF16 | 62.5 GB | | 4-bit | 15.6 GB | - License: Apache 2.0. - Cost to run: none named. - Data it reaches: the files come from Hugging Face, and the model then runs on your own hardware. - Not for: audio input, which the card lists only for E2B, E4B and 12B. ## README ````markdown # gemma-4-31B Reads text and images and writes text, for developers and researchers who want Google DeepMind's largest dense open-weights Gemma 4 model for coding, reasoning and image questions. 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 is the largest dense size in the open Gemma 4 family from Google DeepMind. It reads text and images and writes text. The repository holds about 31.3B parameters as Safetensors files for the transformers library, and it has no audio input. Who it suits: It suits developers and researchers who want an open-weights model for text generation, coding, reasoning and image understanding, run on a consumer GPU or a workstation. ```table Title: Who uses this model Industry | Who uses it | How they use it | Example Software | Developers | Add coding help and reasoning to their own apps | Ask it to review a function Research | Machine learning researchers | Study a dense open-weights model | Compare it with the smaller sizes in the family Product teams | Agent builders | Build assistants that call functions | Give the model a tool and let it decide when to use it ``` Anyone who needs audio input can skip this size, since the card lists audio only for the E2B, E4B and 12B models. Where to use it: - Python scripts that use the transformers library - A consumer GPU or a workstation - Windows, macOS or Linux with Python, for downloading the files ```table Title: Jobs it does Use case | Who | What they get back Text generation | Writers and developers | Written replies to a prompt Coding help | Developers | Code and explanations in text Image questions | Anyone with pictures to check | Text answers about an image you supply Reasoning | Researchers | Step-by-step answers, with thinking modes you can configure ``` How to set it up: You need Python and plenty of disk space. The BF16 files weigh about 62.5 GB, and a 4-bit copy is estimated at about 15.6 GB. - Python with pip - The huggingface_hub package - Free disk space for the weights 1. Install huggingface_hub. 2. Run the download command to save the model files on your computer. 3. Load the files with the transformers library in your own script. ```bash pip install -U huggingface_hub hf download google/gemma-4-31B ``` How to use it: Load the downloaded files with transformers and give the model text, or an image together with text. The system role lets you set ground rules for a conversation. The card says thinking modes are configurable and points to its own pages for the exact settings. Example: You hand the model a screenshot of an error message and ask what it means in plain words. It answers in text. A system message can tell it to keep replies short and formal. For newcomers and experts: Think of it as a model that looks at a picture and a question, then writes an answer. A first small test is one image and one short question. The family also has smaller sizes if this one is too big for your computer. The dense design means all parameters work on every token. The card gives 60 layers, a sliding window of 1024 tokens, a 256K-token context length and a vocabulary of 262K. Attention alternates between local windows and full global layers, with the last layer always global. Global layers share keys and values and use Proportional RoPE to save memory. The vision encoder has roughly 550M parameters. ```flow Text or image goes in Vision encoder reads the image Hybrid attention layers Text comes out ``` Good to know: The family reaches a context window of 256K tokens and supports more than 140 languages. Other people have published 44 quantized and 103 fine-tuned versions of this model. The release has pre-trained and instruction-tuned variants, so check the repository name to see which one you get. ```chart Title: Estimated weight size BF16: 62.5 GB 4-bit: 15.6 GB ``` - License: Apache 2.0. - Cost to run: none named. - Data it reaches: the files come from Hugging Face, and the model then runs on your own hardware. - Not for: audio input, which the card lists only for E2B, E4B and 12B. ## 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: 44,151 - Likes: 596 - License: apache-2.0 - Quantized versions: 46 - Fine-tunes: 103 - Tags: transformers, safetensors, gemma4, image-text-to-text ## Links - Model: https://huggingface.co/google/gemma-4-31B - Files: https://huggingface.co/google/gemma-4-31B/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 ``` 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 on Hugging Face](https://huggingface.co/google/gemma-4-31B) ## 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 (this page) | 44,151 | 596 | 31.3B | apache-2.0 | 2026-07-15 | | [gemma-4-31B-it](https://mindket.com/p/25c128c1-f770-475d-934f-87da192a81e2) | 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-E4B-it](https://mindket.com/p/f3466a64-e796-4818-8ee3-88ee4cf92e3c) | 4,298,348 | 1,850 | 8B | apache-2.0 | 2026-11-03 | | [gemma-4-E2B-it](https://mindket.com/p/52ee89d2-d2e7-4750-b100-90e089c3ec76) | 3,017,059 | 1,129 | 5.1B | apache-2.0 | 2026-11-03 | | [gemma-4-E4B](https://mindket.com/p/83953844-f6f3-401d-a045-c2d846f67785) | 449,316 | 508 | 8B | apache-2.0 | 2026-11-03 | Numbers as Mindket last read them, on 2026-11-12. ## 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 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · epolanski · 2026-09-18](https://news.ycombinator.com/item?id=49751745): Qwen 3.8 Omni Flash: \> If google/gemma-4-31b works, you don't need to overthink it. Up until recently I had a gemini flash 2.0 api deployed that did summarization and translation of news articles/corporate statements fast and cheap and had no reason to update it. If it works fine, this chase of the latest LLM is bit… - [Bluesky · baxin · 2026-11-12](https://bsky.app/profile/baxin.bsky.social/post/3n257zbirwk2r): (2/4) - google/gemma-4-26b-a4b-it:free - google/gemma-4-31b-it:free - heygen/heygen-voice-1-turbo:free - liquid/lfm-2.5-2.6b:free - liquid/lfm-2.5-embedding-350m:free - liquid/lfm-3-1.7b:free - nvidia/llama-nemotron-embed-vl-1b-v2:free - nvidia/llama-nemotron-rerank-vl-1b-v2:free - [Hacker News · Systemerror7A69 · 2026-09-18](https://news.ycombinator.com/item?id=49751564): Qwen 3.8 Omni Flash: My approach to this problem is to just...not try them all. As long as the model you're using solves the problems you have to your satisfaction, there is no need to try any other models, except for financial reasons maybe. So I start with a relatively cheap model (GLM 5.3 flash for me) and as long… - [Bluesky · baxin · 2026-11-12](https://bsky.app/profile/baxin.bsky.social/post/3n23tjsxvcv22): (2/4) - google/gemma-4-31b-it:free - heygen/heygen-voice-1-turbo:free - liquid/lfm-2.5-2.6b:free - liquid/lfm-2.5-embedding-350m:free - liquid/lfm-3-1.7b:free - nvidia/llama-nemotron-embed-vl-1b-v2:free - nvidia/llama-nemotron-rerank-vl-1b-v2:free - nvidia/nemotron-3-embed-1b:free - [Hacker News · mavamaarten · 2026-09-18](https://news.ycombinator.com/item?id=49751471): Qwen 3.8 Omni Flash: I'm wondering, is there a tool or something out there that helps me pick a model, in the vast sea of models out there these days? Every time I need a model for something I see the list on openrouter and I'm completely overwhelmed. I'd love to be able to explain my use case, my cost preferences and… - [Bluesky · baxin · 2026-11-11](https://bsky.app/profile/baxin.bsky.social/post/3n22okfoycf2n): (2/4) - google/gemma-4-31b-it:free - heygen/heygen-voice-1-turbo:free - liquid/lfm-2.5-2.6b:free - liquid/lfm-2.5-embedding-350m:free - liquid/lfm-3-1.7b:free - nvidia/llama-nemotron-embed-vl-1b-v2:free - nvidia/llama-nemotron-rerank-vl-1b-v2:free - nvidia/nemotron-3-embed-1b:free - [Hacker News · akazantsev · 2026-07-19](https://news.ycombinator.com/item?id=48967646): Qwen 3.8: Google makes Gemma 4 31B QAT; that's not a small lab. It's one of the better models out there for consumer hardware. Allows me to run it on a 7900XTX with 64k context. - [Bluesky · baxin · 2026-11-11](https://bsky.app/profile/baxin.bsky.social/post/3mzz7ij2hul2m): (1/4) OpenRouter free models (23) Updated: 2026-11-11 05:21 UTC \#OpenRouter \#LLM https://openrouter.ai/models?q=free - cohere/north-mini-code:free - dots-studio/dots-3-note-preview:free - fish-audio/s2.1-pro-free:free - google/gemma-4-26b-a4b-it:free - google/gemma-4-31b-it:free - [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 · baxin · 2026-11-10](https://bsky.app/profile/baxin.bsky.social/post/3mzy6eab5gd22): (1/4) OpenRouter free models (24) Updated: 2026-11-10 19:28 UTC \#OpenRouter \#LLM https://openrouter.ai/models?q=free - cohere/north-mini-code:free - dots-studio/dots-3-note-preview:free - fish-audio/s2.1-pro-free:free - google/gemma-4-26b-a4b-it:free - google/gemma-4-31b-it:free - [Hacker News · zepearl · 2026-06-27](https://news.ycombinator.com/item?id=48695260): Ask HN: MacBook vs. Dedicated GPU for LLM: I agree. To run an acceptable model (e.g. Qwen/Qwen3.6-27B or google/gemma-4-31B) with a good quantization (minimum Q5) with a good context size (min 64k) you could buy 2 or even 3 GTX 5060 16GiB VRAM for \~550$ each. Fyi the much faster MoE models were useless for my usecases - e.g not able to… - [Bluesky · baxin · 2026-11-10](https://bsky.app/profile/baxin.bsky.social/post/3mzwt5gya3v26): (1/4) OpenRouter free models (24) Updated: 2026-11-10 06:34 UTC \#OpenRouter \#LLM https://openrouter.ai/models?q=free - cohere/north-mini-code:free - dots-studio/dots-3-note-preview:free - fish-audio/s2.1-pro-free:free - google/gemma-4-26b-a4b-it:free - google/gemma-4-31b-it:free ## 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: 2026-07-15 - First published: 2026-03-12 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open google/gemma-4-31B on Hugging Face](https://huggingface.co/google/gemma-4-31B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c35b88b1-d4f0-4377-b018-ecdd3a303439) - [Its page on Mindket](https://mindket.com/p/c35b88b1-d4f0-4377-b018-ecdd3a303439)