Uncensored LLMs: 10 Best Models by GPU Memory (2026)

The best uncensored LLM for most people in 2026 is Huihui-Qwen3.8-27B-abliterated. If your GPU has 16GB of memory or more, start there and you will not go wrong.

This year's uncensored models (LLMs built to answer instead of refusing) are dominated by abliterated versions of Alibaba's Qwen series. Abliteration is a weight-editing technique that removes a model's refusal behavior without retraining.

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What is the difference between uncensored and abliterated?#

"Uncensored" describes an outcome: a model that answers instead of refusing. "Abliterated" names one way to reach that outcome. The two main approaches are finetuning on refusal-free data and abliteration, which edits the weights directly.

For the full definitions, see uncensored vs abliterated and what abliteration is.

Why should you start with Qwen-based abliterated models?#

In 2026, the open-weight ecosystem revolves around Alibaba's Qwen series, and abliterated Qwen builds lead the uncensored field. The line moved from Qwen3.5 to Qwen3.6 in April 2026, then to Qwen3.8 on August 14, 2026. Qwen3.8-27B in particular is now the center of locally runnable uncensored models; its smaller, faster sibling Qwen3.8-Flash-Next has its own uncensored builds.

Three reasons explain Qwen's pull. First, performance: it is especially strong at coding and agentic work, where a model carries out tasks autonomously. Second, its Apache 2.0 license places few restrictions on commercial use. Third, abliteration works well on it, keeping post-edit degradation (KL divergence) small.

Qwen is not the only option. Finetuned families like Dolphin and Hermes, the Mistral line, which is said to suit creative writing, and lightweight Gemma and Llama builds all remain available. A model is chosen as a combination of base model × maker × quantization (the compression that shrinks a model for your hardware).

What are the 10 best uncensored LLMs in 2026?#

The short version: for 16–24GB of GPU memory, Huihui-Qwen3.8-27B-abliterated is the overall pick. For 12–16GB, Gemma 4 26B-A4B uncensored is the frontrunner. For 8–12GB, Huihui-Qwen3.5-9B-abliterated or Llama 3.1 8B abliterated are the realistic choices. The ten entries below run from 8GB GPUs up to one workstation-class model.

1. Huihui-Qwen3.8-27B-abliterated: best overall (huihui-ai, released September 8, 2026)#

The overall frontrunner as of September 2026. It keeps almost all of the latest base model's performance while removing only the refusals.

• Base: Alibaba's Qwen3.8-27B (released August 14, 2026, Apache 2.0). It offers a 262K context (the amount of text it can handle at once) and accepts image and video input.
• How it's made: weight-edited (abliteration). The edit is limited to layers 18–51, leaving the earlier layers untouched to preserve performance. The MTP component, which speeds up responses, and the vision stack are also unmodified.
• Where to get it: on Ollama as huihui_ai/Qwen3.8-abliterated. Hugging Face hosts GGUF builds (Q2_K through Q8_0, plus UD/UD-DW layer-limited variants and a 55.6GB bf16 version). A separate RVN edition, built with a second processing pass, weighs 15.41GiB at Q4_K_M and reports 0–1 refusals out of 100 prompts with a KL divergence of 0.0085, per its distributor's independent measurements.
• Memory guide: 16.8GB at Q4_K_M, 15.3GB at IQ4_XS. Runs comfortably on a 24GB GPU or 32GB of unified memory.
• Who it's for: anyone who wants the all-round strength of the newest base model.

2. Huihui-Qwen3.6-27B-abliterated: the proven standard (April 2026 base)#

A proven staple that predates Qwen3.8. Information about it is abundant, and it is stable to use.

• Base: Qwen3.6-27B (April 2026).
• How it's made: weight-edited (abliteration). A derivative build processed with a tool called Heretic v1.4.0 reports 12 refusals out of 100 prompts and a KL divergence of 0.0118 in the distributor's measurements. The small KL divergence suggests the output stays close to the original model.
• Where to get it: Ollama and Hugging Face GGUF.
• Memory guide: 16–17GB class at Q4. Aim for 16–24GB.
• Who it's for: people who value track record and available information. If you are picking just one, this is the safe choice.

3. Qwen3.6-35B-A3B Uncensored: fast with long context (HauhauCS and others, 2026)#

A MoE model for people who want speed and long documents at once.

• Base: Qwen3.6-35B-A3B. Of its 35B total parameters, only about 3B run at any moment (a MoE, or mixture-of-experts, design that switches between internal specialists), so it feels nimble for its size. It has 40 layers, a 262K context, and handles text, image, and video.
• How it's made: several teams publish independent edited builds. HauhauCS's Aggressive version reports 0 refusals across 465 prompts in the distributor's internal testing. A per-model optimized quantization called K_P ("Perfect") is also available.
• Where to get it: GGUF on Hugging Face (Q2_K equivalent through Q8_0). Also distributed on Ollama.
• Memory guide: about 21GB at Q4_K_M, about 19GB at IQ4_XS. Aim for 16–24GB.
• Who it's for: people who prioritize generation speed and long context handling.

4. Gemma 4 26B-A4B uncensored: the 12–16GB frontrunner (TrevorJS and others, 2026)#

The lead pick in the 12–16GB class. For the 26B-MoE versus 31B-dense builds, see Gemma 4 abliterated.

• Base: Google's Gemma 4 26B-A4B, a MoE design with about 4B of its 26B parameters active.
• How it's made: weight-edited (abliteration) using EGA (Expert-Granular Abliteration), which processes each MoE expert separately. Per the publisher's reported figures, valid refusals fell from 98 out of 100 questions to 1, dropped to 5 out of 686 (0.7%) on a cross-domain test battery, KL divergence came in at 0.09, and no quality loss was observed on harmless responses.
• Where to get it: GGUF on Hugging Face (TrevorJS and others).
• Memory guide: 12–16GB class. Considered practical even on a Mac with 16GB of unified memory.
• Who it's for: people with 12–16GB of GPU memory. Also a good match if you care about speed.

5. Dolphin 3.0 Mistral 24B: the finetuned veteran (Eric Hartford)#

The original finetuned uncensored line, and a long-standing staple.

• Base: Mistral Small 24B family.
• How it's made: finetuned on refusal-free data. Handles instruction following, coding, and function calling, and an R1-style reasoning variant exists.
• Where to get it: Ollama and Hugging Face. The wide range of quantization options is a strength.
• Memory guide: 14–16GB class at Q4. Aim for 16–24GB.
• Who it's for: people who prefer a retrained, stability-oriented model over a weight-edited one. A good pick if you want one model for everything.

6. Huihui-Mistral-Small-3.2-24B-abliterated: the writer's pick (huihui-ai)#

A favorite among creative writers and roleplayers. Its strength is "scene consistency," the quality that matters most in long-form generation.

• Base: Mistral Small 3.2 24B.
• How it's made: weight-edited (abliteration). The design edits only the text pathway to preserve prose quality, and the distributor says it stays consistent across long scenes and responds well to steering via system prompt.
• Where to get it: Ollama and Hugging Face. Also served through platforms such as Featherless.
• Memory guide: 14–16GB class at Q4 quantization. Aim for 16–24GB of VRAM or unified memory.
• Who it's for: fiction, creative writing, and roleplay. Lowering the temperature (output randomness) to around 0.3 is said to stabilize it.

7. Nous Hermes 4: steer-it-yourself (Nous Research, 14B / 36B / 70B)#

A finetuned model built to follow the user's instructions rather than a corporate ethics code. For the lab behind it, see Nous Research and Hermes.

• Base: three sizes (14B, 36B, and 70B).
• How it's made: finetuned. The publisher calls this policy "neutral alignment": instead of embedding prohibitions into the model, it follows whatever policy you write in the system prompt. Nous Research reports top-tier results on evaluations measuring low refusal rates.
• Where to get it: Ollama and Hugging Face.
• Memory guide: the 14B runs at about 12GB at Q4; the 36B needs around 24GB.
• Who it's for: people who would rather design the model's behavior themselves than chase absolute zero refusals.

8. Huihui-Qwen3.5-9B-abliterated: the lightweight pick (huihui-ai, March 2026)#

A lightweight release from March 2026. It runs on 12GB of memory or less and prioritizes speed.

• Base: Qwen3.5 9B.
• How it's made: weight-edited (abliteration), balancing low weight with low refusal rates.
• Where to get it: Ollama and Hugging Face.
• Memory guide: 8–12GB class.
• Who it's for: people who want a light first try, and anyone who values response speed. A temperature around 0.5 is said to work well.

9. Llama 3.1 8B abliterated: the classic first model (mlabonne and others)#

A standard first model.

• Base: Meta's Llama 3.1 8B.
• How it's made: weight-edited (abliteration). Widely referenced as the community's baseline build.
• Where to get it: Ollama (mannix/llama3.1-8b-abliterated and others) and GGUF on Hugging Face.
• Memory guide: 5.7GB at Q5_K_M. The Q4_K_M build fits on 8GB-class GPUs.
• Who it's for: first-timers on a GPU around 8GB. Tool-use derivative builds exist as well.

10. Huihui-GLM-5.1-abliterated: the upper limit (huihui-ai, bonus entry)#

The largest open abliterated model available right now. Consumer hardware cannot run it.

• Base: GLM-5.1, a 754B MoE model.
• How it's made: weight-edited (abliteration).
• Where to get it: GGUF on Hugging Face.
• Memory guide: even the smallest IQ2_M quantization comes in around 236GB. You need a Mac Studio with 256GB or more, a multi-GPU rig, or a setup built around CPU offloading.
• Who it's for: people with workstation-class hardware, for research and validation purposes.

Which uncensored model fits your GPU?#

Work through three steps to go from ten models to one: check your GPU memory, pick a size bracket from that number, then narrow to a single model by use case. GPU memory sets the upper limit on everything else, so check it first.

Step 1: Check your GPU memory. Find out how much VRAM your PC's GPU has. On a Mac, the unified memory figure is the guide.

Step 2: Use that number to pick a size bracket.

• 16–24GB: Qwen3.8-27B, Qwen3.6-27B, and Qwen3.6-35B-A3B come into view.
• 12–16GB: Gemma 4 26B-A4B.
• 8–12GB: Qwen3.5-9B or Llama 3.1 8B are the realistic options.

Step 3: Narrow to one by use case.

• All-round strength on the newest base: Qwen3.8-27B abliterated.
• Track record and stability: Qwen3.6-27B abliterated.
• Response speed and long documents: Qwen3.6-35B-A3B.
• Fiction and roleplay: Mistral Small 3.2 abliterated.
• Designing the behavior yourself: Hermes 4.
• A first model: Llama 3.1 8B abliterated is the easiest to handle.

How do you run uncensored models with Ollama or LM Studio?#

The two standard tools are Ollama, which runs from the command line, and LM Studio, which works through a graphical interface. Both are free. With Ollama, one command downloads and starts a model. With LM Studio, you search by model name, pick a quantization level from the list, and download.

If you are comfortable with the command line, Ollama is the easier path. After installing, naming the model is all it takes.

ollama run huihui_ai/Qwen3.8-abliterated

Our local LLM beginner's guide walks through installation, and our quantization explainer covers how to pick a quantization level.

If you would rather not download anything, our guide to choosing an uncensored AI compares the three ways to get one: local, API, and a hosted service.

Two cautions apply:

• Downloads take time. Model files run from a few GB to tens of GB, so start the download somewhere with a good connection.
• Models with a "thinking" feature give longer responses. They print their reasoning before answering, and if that feels long, some setups let you switch it off.

What do third-party benchmarks say about uncensored models?#

Two July 2026 roundups from outside the model makers give independent numbers. Atomic Chat's guide (July 17, 2026) compares five models as follows.

Over-refusal is the rate at which a model declines requests it should answer, so lower is better. MMLU is a benchmark of general knowledge, where higher is better. tok/s is tokens per second, a measure of generation speed. The same guide lists the Gemma build at 7.7GB and Dolphin 3.0 8B at 5.7GB.

Featherless AI's roundup (July 24, 2026) reports 0.7% refusal across four test sets for gemma-4-26B-A4B-it-uncensored, with quality effectively unchanged.

A low refusal rate does not make a model better overall. Dolphin-Mistral 24B has the highest MMLU in the table but runs at 10 tok/s, and the lowest over-refusal (Gemma Abliterated 9B) comes with a lower MMLU than that 24B model. These figures are third-party measurements, and they change with testing conditions, so treat them as a guide rather than a ranking.

What should you check before choosing a model?#

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#

What is the best uncensored LLM in 2026?#

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If you are unsure, choose Huihui-Qwen3.8-27B-abliterated. With 16GB or more of GPU memory, it is the safest starting point, because it keeps nearly all of the newest Qwen base model's performance while removing refusals. With 12–16GB, Gemma 4 26B-A4B uncensored leads, and at 8–12GB Qwen3.5-9B abliterated or Llama 3.1 8B abliterated are the realistic picks.

What is the difference between uncensored and abliterated?#

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"Uncensored" is the result (the model answers instead of refusing), and "abliterated" is one way to reach it. See uncensored vs abliterated for the full comparison.

Which uncensored model works with limited GPU memory?#

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For 12–16GB, Gemma 4 26B-A4B uncensored is the frontrunner and is considered practical even on a 16GB unified-memory Mac. For 8–12GB, Huihui-Qwen3.5-9B-abliterated and Llama 3.1 8B abliterated are realistic. The Llama build needs just 5.7GB at Q5_K_M quantization, and its Q4_K_M version fits 8GB-class GPUs.

Can I try uncensored models without downloading them?#

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Yes. Deepent Research, a chat service that runs uncensored and abliterated models, works in your browser with no GPU and no install. You can try it free without signing up, and in the logged-out trial your conversation is not saved. A hosted service is the quickest way to test how such a model behaves before you download one.

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.