How to Run Uncensored Models With Ollama (2026 Guide)

Ollama runs an uncensored model on your own machine with two commands. Install it, then pull an abliterated build, such as ollama pull huihui_ai/Qwen3.8-abliterated. Which build suits you depends on GPU memory: 16GB or more suits a 27B-class model, and 8 to 12GB suits a lighter 8B or 9B build.

Ollama is an open-source command-line tool that downloads and runs models on your own hardware. You give it a model name and it handles the download and the run. Uncensored builds are published in the community library under namespaces such as huihui_ai.

"Uncensored" here means an abliterated build, one whose refusal behavior has been edited out of the weights. That edit has a cost, so read how abliteration works before you rely on the answers.

How do you run an uncensored model with Ollama?#

Install Ollama, then download a model with ollama pull and start it with ollama run, using a name from the Ollama library such as huihui_ai/Qwen3.8-abliterated. Both commands take the model name as their argument, and ollama run downloads the model first if it is not already on the machine.

For example:

ollama pull huihui_ai/Qwen3.8-abliterated
ollama run huihui_ai/Qwen3.8-abliterated

How do you install Ollama?#

Download the installer for Windows, macOS or Linux from the official documentation at docs.ollama.com and run it. The install adds an ollama command that you use from a terminal.

How do you find an uncensored model name?#

Model names are listed in the Ollama library. Community namespaces publish the edited builds, and the name keeps the form namespace/model, as in huihui_ai/Qwen3.8-abliterated. The entry page also shows which tags are available, so you can confirm the quantization level before you download.

What is a Modelfile for?#

A Modelfile is a short text file that stores the settings you want applied every time, such as a system prompt. With one, you save your own named variant instead of repeating the options each session, which suits a build you use often.

Which uncensored model should you pick for your VRAM?#

Choose by GPU memory first and quantization second. With 16GB of VRAM or more, an edited Qwen3.8-27B is the standard pick; 12 to 16GB points to Gemma 4 26B-A4B uncensored, and 8 to 12GB to Qwen3.5-9B abliterated or Llama 3.1 8B abliterated. Then use Q4_K_M unless you have room to step up.

VRAM is the memory on your graphics card, and the model has to fit there to run. The table gives published sizes as a starting point.

The wider roundup of uncensored models covers the individual builds and who publishes them.

How do you choose the quantization level?#

Q4_K_M is the default balance between file size and quality, so start there. If you have spare VRAM, step up to Q5_K_M or Q6_K, which come closer to the original at the cost of a larger file. At the same VRAM, a larger model at Q4 usually beats a smaller model at Q8. The quantization explainer covers how to read the labels.

What should you check before you download a build?#

Check four things: the base model, who published the build, what licence it carries, and whether a GGUF exists. The base model sets the size and capabilities, the publisher determines which edit method was used, the licence sets what you may do with the build, and a missing GGUF limits which quantization levels you can run.

• The abliterated name usually embeds the base, as Qwen3.8-27B does, so the name already tells you the size and capabilities you get.
• A shared base name does not mean shared behavior. Different publishers use different tools and edit different parts, so two builds carrying the Qwen3.8-27B name can behave like separate models (how the editions differ).
• Qwen3.8-27B is published under Apache 2.0. Check on the model card whether an edited build carries the base model's licence before you plan to use it commercially.
• Lightweight versions are community conversions, so check whether a GGUF exists for the build you want before you rely on a low quantization level.

What goes wrong when you run an uncensored model locally?#

Three things usually go wrong: the model will not load, the file is not the quantization you meant to pull, and memory runs out when you push the context long. The usual remedy is to move to a smaller model or a lighter quantization and to leave room for context on top of the model file.

• When the model will not load, the build is usually larger than your available memory. Move one size down or choose a lighter quantization level, then try again.
• The tag in the model name decides which file you get, so pulling a build without checking the tag can leave you with a file that is too large for your card or lighter than you intended.
• Context is the text you feed in at once, and it needs working memory on top of the model file itself. A rough estimate is 10 to 20% headroom above the published model size.

If you would rather not manage any of this on your own hardware, the local LLM guide compares running models locally with using a hosted service.

What is Deepent Research?#

Deepent Research is a browser-accessible LLM chat service built to refuse as little as possible. It keeps going with research that a standard AI would cut off mid-answer.

• It refuses nothing. We run uncensored and abliterated models with guardrails reduced as far as possible, so prompts a standard AI answers with "I can't help with that" still get a response (Qwen3.5 / Qwen3.8-27B-abliterated, depending on your plan).
• Your input is never used for training. What you look up is used only to provide the service, never to train AI or for any other purpose. Personal data is protected, and our privacy policy is public.
• Try it free. No GPU and no install, just open a browser. In the logged-out trial your conversation isn't saved, so you can start casually.

→ Try it free, no sign-up: https://deepentresearch.com

No question can stop research.

Frequently asked questions#

Is Ollama free, and how much GPU memory do you need?#

⌄

Ollama is open source and free to install, and it runs models on your own machine rather than on a provider's servers. The GPU memory you need depends on the model. Llama 3.1 8B takes 5.7GB at Q5_K_M, while an edited Qwen3.8-27B takes about 16.8GB at Q4_K_M, so the 27B class needs 16GB of VRAM or more.

Which uncensored model should you start with on Ollama?#

⌄

Start with the largest model your GPU memory can hold at Q4_K_M. If you have 16GB or more, that means an abliterated Qwen3.8-27B at about 16.8GB. With 12 to 16GB, Gemma 4 26B-A4B uncensored is the fit, and with 8 to 12GB, Qwen3.5-9B abliterated or Llama 3.1 8B abliterated.

Do you need ollama pull before ollama run?#

⌄

No. ollama run downloads the model if it is not already on the machine, so one command is enough to start. ollama pull is useful when you want the download finished before you chat, or when you want to fetch a model without opening a session.

Can you use an uncensored model commercially?#

⌄

It depends on the licence the build carries. Qwen3.8-27B is published under Apache 2.0, which is commercial-friendly, and an edited build may inherit the terms of its base model. Other base models set their own conditions, so check the model card on the page you download from before you use a build for commercial work.

Disclaimer#

This article was drafted with AI assistance and then reviewed, fact-checked, and edited by the Deepent Research team before publication.
It is provided for educational and research purposes and is not intended to encourage unlawful activity or crime.
Where figures are cited, they come from third-party public reports and vendor-published values and may vary with measurement conditions.