--- title: "gpt-oss-120b-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/fb2a9aca-79b2-40c9-a762-9bb74fe09dd8" updated: "2026-11-13" --- # gpt-oss-120b-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs OpenAI's open-weight gpt-oss-120b, a 117B model with 5.1B active parameters, from GGUF files by Unsloth that carry its fixes. Built for reasoning, agent tasks and developer use. gpt-oss-120b-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by unsloth ## About ### What it is This repository holds GGUF versions of gpt-oss-120b, the larger open-weight model in OpenAI's gpt-oss series. It has 117B parameters, with 5.1B active at a time. Unsloth made the files and says every GGUF carries its fixes. The F16 file keeps the model's original precision. ### Who it suits Developers who want a strong open reasoning model they can run and customize themselves. The weights are estimated at about 58.4 GB at 4-bit, so this suits well-equipped machines. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Build agents that call functions and run code | Let the model choose a tool for a request | | Research | AI researchers | Study reasoning with a visible chain of thought | Inspect how the model reaches an answer | | Self-hosting | Teams with large hardware | Run a general-purpose model without a hosted API | Serve it inside their own network | | Customization | ML engineers | Fine-tune the model for a specific use case | Adapt it to a company's own tasks | People with a small laptop can skip this size, because the smaller gpt-oss-20b is the one aimed at local use. ### Where to use it - Tools that load GGUF files, with Unsloth's guide as the card's pointer - A machine that can hold the weights, which the card says fit on a single H100 GPU in native form - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Reasoning tasks | Developers and researchers | Answers with an adjustable depth of reasoning | | Agent work | Developers | Function calls, web browsing and Python code execution | | Structured output | Developers | Answers that follow a set format | ### How to set it up - Python, for the Hugging Face hub tools - A runner that loads GGUF files - Disk space and memory for the file you choose, with the 4-bit weights estimated at about 58.4 GB 1. Install the Hugging Face hub tools with pip. 2. Download the files with the hf command. 3. Load a file in your runner, following Unsloth's guide for gpt-oss. ```sh pip install -U huggingface_hub hf download unsloth/gpt-oss-120b-GGUF ``` ### How to use it The card shows no run command in the part given. The model was trained on OpenAI's harmony response format and works correctly only with that format. A chat template applies it for you. You can set the reasoning effort to low, medium or high, depending on how hard the task is and how much delay you accept. | Tool | What it does | | --- | --- | | Reasoning effort | Low, medium or high, to trade depth for speed | | Function calling | Lets the model call functions you define | | Web browsing | A native tool the model can use for browsing | | Python execution | A native tool for running Python code | | Structured outputs | Keeps answers in a set format | ### Example A developer sets the reasoning effort to high and asks the model a hard question. The model thinks through it and returns an answer. The full chain of thought is available for debugging, but the card says it is not meant for end users. ### For newcomers and experts This is a big model, so begin with a small test on a machine that can hold it. Use the chat template so the harmony format is applied for you. Check the answer, then raise or lower the reasoning effort. The model is a mixture-of-experts with 117B parameters and 5.1B active. OpenAI trained its MoE layer in native MXFP4 precision, which is why the 120b model fits on one H100 GPU. This repo's F16 file keeps the original precision, and the other files are smaller quantizations. Format the chat in the harmony format → Choose a reasoning effort → The model reasons, then answers → A tool call, if any, comes back as text ### Good to know - Format: The model works correctly only with the harmony response format. - Reasoning: The chain of thought is fully visible and meant for debugging, not for showing to end users. - Sizes: The 120b model has 117B parameters, and the smaller gpt-oss-20b has 21B with 3.6B active. - License: Apache 2.0, with no copyleft limits or patent risk, as the card puts it. - Cost to run: none named. You need hardware that can hold the weights, and no paid service appears in the source. - Data it reaches: Files on your computer. The web browsing and code execution tools are native features of the model. - Not for: Showing raw reasoning to end users, and running without the harmony format. ## README ````markdown # gpt-oss-120b-GGUF Runs OpenAI's open-weight gpt-oss-120b, a 117B model with 5.1B active parameters, from GGUF files by Unsloth that carry its fixes. Built for reasoning, agent tasks and developer use. Made by unsloth 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: This repository holds GGUF versions of gpt-oss-120b, the larger open-weight model in OpenAI's gpt-oss series. It has 117B parameters, with 5.1B active at a time. Unsloth made the files and says every GGUF carries its fixes. The F16 file keeps the model's original precision. Who it suits: Developers who want a strong open reasoning model they can run and customize themselves. The weights are estimated at about 58.4 GB at 4-bit, so this suits well-equipped machines. ```table Industry | Who uses it | How they use it | Example Software | Developers | Build agents that call functions and run code | Let the model choose a tool for a request Research | AI researchers | Study reasoning with a visible chain of thought | Inspect how the model reaches an answer Self-hosting | Teams with large hardware | Run a general-purpose model without a hosted API | Serve it inside their own network Customization | ML engineers | Fine-tune the model for a specific use case | Adapt it to a company's own tasks ``` People with a small laptop can skip this size, because the smaller gpt-oss-20b is the one aimed at local use. Where to use it: - Tools that load GGUF files, with Unsloth's guide as the card's pointer - A machine that can hold the weights, which the card says fit on a single H100 GPU in native form - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Reasoning tasks | Developers and researchers | Answers with an adjustable depth of reasoning Agent work | Developers | Function calls, web browsing and Python code execution Structured output | Developers | Answers that follow a set format ``` How to set it up: - Python, for the Hugging Face hub tools - A runner that loads GGUF files - Disk space and memory for the file you choose, with the 4-bit weights estimated at about 58.4 GB 1. Install the Hugging Face hub tools with pip. 2. Download the files with the hf command. 3. Load a file in your runner, following Unsloth's guide for gpt-oss. ```sh pip install -U huggingface_hub hf download unsloth/gpt-oss-120b-GGUF ``` How to use it: The card shows no run command in the part given. The model was trained on OpenAI's harmony response format and works correctly only with that format. A chat template applies it for you. You can set the reasoning effort to low, medium or high, depending on how hard the task is and how much delay you accept. ```table Tool | What it does Reasoning effort | Low, medium or high, to trade depth for speed Function calling | Lets the model call functions you define Web browsing | A native tool the model can use for browsing Python execution | A native tool for running Python code Structured outputs | Keeps answers in a set format ``` Example: A developer sets the reasoning effort to high and asks the model a hard question. The model thinks through it and returns an answer. The full chain of thought is available for debugging, but the card says it is not meant for end users. For newcomers and experts: This is a big model, so begin with a small test on a machine that can hold it. Use the chat template so the harmony format is applied for you. Check the answer, then raise or lower the reasoning effort. The model is a mixture-of-experts with 117B parameters and 5.1B active. OpenAI trained its MoE layer in native MXFP4 precision, which is why the 120b model fits on one H100 GPU. This repo's F16 file keeps the original precision, and the other files are smaller quantizations. ```flow Format the chat in the harmony format Choose a reasoning effort The model reasons, then answers A tool call, if any, comes back as text ``` Good to know: - Format: The model works correctly only with the harmony response format. - Reasoning: The chain of thought is fully visible and meant for debugging, not for showing to end users. - Sizes: The 120b model has 117B parameters, and the smaller gpt-oss-20b has 21B with 3.6B active. - License: Apache 2.0, with no copyleft limits or patent risk, as the card puts it. - Cost to run: none named. You need hardware that can hold the weights, and no paid service appears in the source. - Data it reaches: Files on your computer. The web browsing and code execution tools are native features of the model. - Not for: Showing raw reasoning to end users, and running without the harmony format. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 117B - Weights: about 58.4 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 81,029 - Likes: 303 - License: apache-2.0 - Based on: openai/gpt-oss-120b - Tags: transformers, gguf, gpt_oss, text-generation, openai, unsloth, mxfp4, conversational ## Links - Model: https://huggingface.co/unsloth/gpt-oss-120b-GGUF - Files: https://huggingface.co/unsloth/gpt-oss-120b-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download unsloth/gpt-oss-120b-GGUF ``` 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 [unsloth/gpt-oss-120b-GGUF on Hugging Face](https://huggingface.co/unsloth/gpt-oss-120b-GGUF) ## 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 | | --- | --- | --- | --- | --- | --- | | gpt-oss-120b-GGUF (this page) | 81,029 | 303 | 117B | apache-2.0 | 2025-08-25 | | [gpt-oss-20b-GGUF](https://mindket.com/p/99b8d5c9-4750-4943-807b-082810629e1f) | 614,134 | 909 | 20.9B | apache-2.0 | 2025-12-19 | | [gpt-oss-20b](https://mindket.com/p/d353330b-254d-4db1-9a53-f0b1f8fe2945) | 5,685,450 | 5,299 | 20.9B | apache-2.0 | 2025-08-26 | | [gpt-oss-120b](https://mindket.com/p/8cdacc53-33c7-4a1c-abfa-44e2a4277d1d) | 2,435,242 | 5,509 | 117B | apache-2.0 | 2025-08-26 | | [gpt-oss-safeguard-20b](https://mindket.com/p/fcd561d5-43eb-40c8-a39a-415fc6da6f43) | 24,325 | 293 | 21.5B | apache-2.0 | 2026-01-14 | | [gpt-oss-safeguard-120b](https://mindket.com/p/d0abc616-5e84-4dc6-84fa-535eac5df9c1) | 16,400 | 128 | 117B | apache-2.0 | 2025-10-29 | 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 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · KronisLV · 2026-05-20](https://news.ycombinator.com/item?id=48211279): Qwen3.7-Max: The Agent Frontier: There definitely have been some options in the past, cool to see them. Oddly enough, though, Qwen 3.6 35B A3B and Gemma got some really good reviews, despite being way smaller than any of these ones. Qwen 3.5, 122B A10B: https://huggingface.co/unsloth/Qwen3.5-122B-A10B-GGUF Qwen Coder Next, 80B… - [Bluesky · emily-3.5-0.4B:IQ1\_XXXS · 2025-11-14](https://bsky.app/profile/emily.news/post/3m5mmllfgrc2g): while I wait for q8\_K\_XL to download, I'm putting my most powerful model on the case: hf.co/unsloth/gpt-oss-120b-GGUF it chugs along at something like 5 tokens per second, so I'm still waiting - [Hacker News · nyrikki · 2026-02-18](https://news.ycombinator.com/item?id=47064279): The Future of AI Software Development: It is crazy to me that it is that slow, 4 bit quants don't lose much with Qwen3 coder next and unsloth/Qwen3-Coder-Next-UD-Q4\_K\_XL gets 32 tps with a 3090 (24gb) as a VM with 256k context size with llama.cpp Same with unsloth/gpt-oss-120b-GGUF:F16 gets 25 tps and gpt-oss20b gets 195 tps\!\!\! The… - [Hacker News · predkambrij · 2026-02-03](https://news.ycombinator.com/item?id=46878920): Qwen3-Coder-Next: 17t/s on a laptop with 6GB VRAM and DDR5 system memory. Maximum of 100k context window (then it saturates VRAM). Quite amazing, but tbh I'll still use inference providers, because it's too slow and it's my only machine with "good" specs :) cat docker-compose.yml services: llamacpp: volumes: -… - [Hacker News · aseipp · 2025-11-10](https://news.ycombinator.com/item?id=45881049): Asus Ascent GX10: Unfortunately comparing tok/sec right now in a vacuum and especially across weeks of time is kind of pointless. Everything is still evolving; there were patches within days that bumped GB10 performance by double digit percentiles in some frameworks. You just kind of have to accept things are a… - [Hacker News · danielhanchen · 2025-08-22](https://news.ycombinator.com/item?id=44988087): DeepSeek-v3.1: For GPT OSS in particular, OpenAI only released the MoEs in MXFP4 (4bit), so the "unquantized" version is 4bit MoE + 16bit attention - I uploaded "16bit" versions to https://huggingface.co/unsloth/gpt-oss-120b-GGUF, and they use 65.6GB whilst MXFP4 uses 63GB, so it's not that much difference -… ## 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: 2025-08-25 - First published: 2025-08-05 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open unsloth/gpt-oss-120b-GGUF on Hugging Face](https://huggingface.co/unsloth/gpt-oss-120b-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/fb2a9aca-79b2-40c9-a762-9bb74fe09dd8) - [Its page on Mindket](https://mindket.com/p/fb2a9aca-79b2-40c9-a762-9bb74fe09dd8)