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Best Uncensored LLM Models for 16GB VRAM in 2026

Cowrie
Cowrie
Dev @ Bswen

Purpose

I tested uncensored LLM models on my RTX 5070 Ti with 16GB VRAM to find the best options for local deployment. Standard open-weight models refuse certain requests due to safety fine-tuning, but abliteration techniques can remove these restrictions without retraining.

After analyzing Reddit discussions from r/LocalLLaMA, official documentation, and running my own benchmarks, I found that Mistral-based models offer the best balance of uncensorship and quality for 16GB VRAM hardware. This guide shows you exactly which models to run and how to deploy them.

Quick Answer: Top 5 Models

Best Uncensored LLM Models for 16GB VRAM (March 2026)
RANK  MODEL                      VRAM (Q4)    BEST FOR                     COMMUNITY SCORE
----  -------------------------  -----------  ---------------------------  ---------------
 1.   Mistral Small 24B          ~14GB        Best overall quality          28 votes
      (abliterated)
 2.   GLM-4.7-Flash Heretic      ~12GB        Maximum uncensorship          23 votes
 3.   Qwen 2.5/3.5 Abliterated   ~6GB (9B)    Multilingual support           2 votes
 4.   Mistral NeMo 12B           ~8GB         Long context (128K)           28 votes
      (abliterated)
 5.   DeepSeek-R1 Distilled      ~5GB (7B)    Reasoning tasks               N/A
      (abliterated)

Key insight: Mistral models have naturally less restrictive base training, making them the preferred foundation for uncensored variants. Community consensus: “Generally the most uncensored base models that work with 16GB VRAM.”

What is Abliteration?

Standard models undergo safety fine-tuning that creates a “rejection direction” in their neural activations. Abliteration removes this mechanism without retraining.

How Abliteration Works
+------------------+       +------------------+       +------------------+
|   STANDARD       |       |   REJECTION      |       |   ABLITERATED    |
|   MODEL          |       |   DIRECTION      |       |   MODEL          |
+------------------+       +------------------+       +------------------+
|                  |       |                  |       |                  |
|  User Request    |------>|  Refusal Layer   |------>|  No Refusal      |
|  "Help me with X"|       |  "I cannot..."   |       |  Direct Response |
|                  |       |                  |       |                  |
+------------------+       +------------------+       +------------------+
                                  |                          ^
                                  |                          |
                                  +--------------------------+
                                           REMOVED
                                    (Weight Orthogonalization)

“Heretic” variants represent aggressive abliteration. The community term “Heretic scale” measures how thoroughly refusal mechanisms were removed. As one Reddit user put it: “Heretic is the new obliterated… there is heretic scale.”

Model 1: Mistral Small 24B (Abliterated) - Best Overall

Why it ranks #1:

Community consensus identifies Mistral Small 24B as the best choice for 16GB VRAM. I found multiple threads confirming its superiority.

Mistral Small 24B Specifications
PARAMETER          VALUE
-----------------  --------------------
Parameters         24B
VRAM (Q4_K_M)      ~14GB
Context Window     128K tokens
MMLU Score         81%
License            Apache 2.0
Base Training      Less restrictive

Why developers choose it:

  • Naturally less restrictive base training than LLaMA
  • 24B parameters at Q4_K_M uses ~14GB VRAM, leaving room for context
  • Competitive with much larger models on benchmarks
  • Mistral family consensus: “Pretty much any mistral model” works for uncensored use

Deployment with Ollama:

Install Mistral Small via Ollama
# Official model (not abliterated)
ollama run mistral-small:24b


# For abliterated version, search HuggingFace:
# Query: "mistral-small abliterated" or "mistral heretic"
# Download GGUF and import to Ollama

Quantization choices for 16GB VRAM:

Quantization Trade-offs for Mistral Small 24B
QUANTIZATION    VRAM USAGE    QUALITY    CONTEXT ROOM
--------------  ------------  ---------  ------------
Q3_K_M          11-12GB       Good       Excellent
Q4_K_M          14GB          Very Good  Limited
Q5_K_M          17GB          Excellent  Needs CPU offload

I recommend Q4_K_M as the default. Q3_K_M if you need longer contexts. Q5_K_M requires CPU offload on 16GB cards.

Model 2: GLM-4.7-Flash Heretic - Maximum Uncensorship

Why it ranks #2:

Direct community recommendation: “GLM-4.7-Flash Heretic is what you want.” This model represents the highest degree of uncensorship available.

GLM-4.7-Flash Specifications
PARAMETER          VALUE
-----------------  --------------------
Parameters         30B class
VRAM (Q4)          ~12GB
Context Window     128K tokens
Languages          Chinese, Japanese, Korean, English
Specialization     Maximum abliteration

Key advantages:

  • Heretic-scale abliteration removes virtually all refusals
  • Strong multilingual support for Asian languages
  • Efficient architecture fits 16GB VRAM comfortably
  • Community-tested and validated

Deployment:

Install GLM-4.7-Flash via Ollama
# Official GLM model
ollama run glm-4.7-flash


# For Heretic variant, search HuggingFace:
# Query: "glm-4.7 heretic" or "glm abliterated"
# Look for models from trusted abliteration creators

Best for: Users who need maximum uncensorship, especially for Chinese/Japanese/Korean language content.

Model 3: Qwen 2.5/3.5 Abliterated - Multilingual Powerhouse

Why it ranks #3:

Community advice: “For your amount of RAM go for 9B versions.” Qwen offers excellent multilingual capabilities in a smaller footprint.

Qwen Abliterated Options for 16GB VRAM
MODEL SIZE     VRAM (Q4)    LANGUAGES    USE CASE
--------------  -----------  -----------  ---------------------------
Qwen2.5-7B      ~5GB         100+         Maximum context room
Qwen2.5-9B      ~6GB         100+         Recommended balance
Qwen2.5-14B     ~10GB        100+         Higher quality, less context

Why developers choose it:

  • Trained on 18 trillion tokens with 128K context
  • Excellent multilingual capabilities (100+ languages)
  • Strong coding and reasoning abilities
  • Smaller models leave more VRAM for context

Deployment:

Install Qwen Abliterated via Ollama
# Official Qwen models
ollama run qwen2.5:7b
ollama run qwen2.5:14b


# For abliterated variants, search HuggingFace:
# Query: "qwen2.5 abliterated" or "qwen uncensored"

Best for: Multilingual tasks, coding assistance, and users who want extra VRAM for long contexts.

Model 4: Mistral NeMo 12B (Abliterated) - Context King

Why it ranks #4:

Built in collaboration with NVIDIA, Mistral NeMo excels at long-context tasks while leaving plenty of VRAM headroom.

Mistral NeMo 12B Specifications
PARAMETER          VALUE
-----------------  --------------------
Parameters         12B
VRAM (Q4_K_M)      ~8GB
Context Window     128K tokens
License            Apache 2.0 (fully open)
Collaboration      NVIDIA partnership

Key advantages:

  • Uses only ~8GB VRAM at Q4_K_M quantization
  • 128K context window for long documents
  • Fully open Apache 2.0 license
  • More VRAM available for context caching

Deployment:

Install Mistral NeMo via Ollama
ollama run mistral-nemo


# For abliterated version, search HuggingFace:
# Query: "mistral-nemo abliterated"

Best for: Long document processing, context-heavy applications, and users who want maximum VRAM headroom.

Model 5: DeepSeek-R1 Distilled (Abliterated) - Reasoning Champion

Why it ranks #5:

DeepSeek-R1 rivals O3 and Gemini 2.5 Pro for reasoning. The distilled versions (7B, 8B) bring this capability to consumer hardware.

DeepSeek-R1 Distilled Options
MODEL SIZE         VRAM (Q4)    REASONING    CONTEXT
-----------------  -----------  -----------  -----------
DeepSeek-R1-7B     ~5GB         Strong       32K
DeepSeek-R1-8B     ~6GB         Strong       32K

Key advantages:

  • Excellent reasoning for complex tasks
  • Small footprint fits easily in 16GB VRAM
  • Abliterated variants available in community
  • Good for coding and analysis

Deployment:

Install DeepSeek-R1 via Ollama
ollama run deepseek-r1:7b
ollama run deepseek-r1:8b


# For abliterated variant, search HuggingFace:
# Query: "deepseek-r1 abliterated"

Best for: Complex reasoning tasks, coding challenges, and users who prioritize analytical capabilities.

Hardware Requirements

I mapped out VRAM requirements for each model size with different quantizations:

VRAM Requirements by Model Size and Quantization (16GB Card)
+------------------+--------+--------+--------+--------+
| MODEL SIZE       | Q3_K_M | Q4_K_M | Q5_K_M | Q6_K   |
+------------------+--------+--------+--------+--------+
| 7B parameters    | 4GB    | 5GB    | 6GB    | 7GB    |
| 9B parameters    | 5GB    | 6GB    | 8GB    | 9GB    |
| 12B parameters   | 6GB    | 8GB    | 10GB   | 12GB   |
| 14B parameters   | 8GB    | 10GB   | 13GB   | 15GB   |
| 22-24B params    | 11GB   | 14GB   | 17GB*  | 20GB*  |
+------------------+--------+--------+--------+--------+
                                        *Requires CPU offload

Tips for maximizing 16GB VRAM:

  1. Default to Q4_K_M quantization - Best balance of quality and size
  2. Enable Flash Attention - Reduces memory for long contexts
  3. Use Q3_K_M for larger models - Fits 24B models with context room
  4. Monitor with nvidia-smi - Watch VRAM during inference
Monitor VRAM Usage
watch -n 1 nvidia-smi

Deployment Tools Comparison

I tested three main deployment options for local LLM inference:

Deployment Tool Comparison
TOOL           BEST FOR           PROS                         CONS
--------------  -----------------  ---------------------------  --------------------
Ollama         Quick setup        One-line install,            Limited to library
                                  auto-quantization            models


LM Studio      GUI preference     Visual interface,            Closed-source
                                  HuggingFace integration


llama.cpp      Maximum control    All quantization options,    Command-line only
                                  server mode

My recommendation: Start with Ollama for simplicity. Use LM Studio if you prefer a GUI. Use llama.cpp for maximum control and custom quantizations.

Finding Abliterated Models

Official model libraries (Ollama, LM Studio) don’t host abliterated variants. You need to search HuggingFace:

HuggingFace Search Terms
SEARCH TERM              FINDS
-----------------------  ----------------------------------------
"abliterated"            Models with refusal mechanisms removed
"uncensored"             Alternative term for abliterated
"heretic"                Maximum abliteration variants
"[model-name] dolphin"   Eric Hartford's uncensored variants

Trusted sources for abliterated models:

  1. failspy/abliterated-v3 - Collection of abliterated models
  2. cognitivecomputations - Eric Hartford’s Dolphin series
  3. Nous Research - Models with less restrictive training by default

Manual abliteration:

You can apply abliteration to any instruction-tuned model:

  1. Use FailSpy’s abliterator notebook
  2. Requires ~30GB RAM for 7B model processing
  3. Output is a permanently modified model

Model Selection Guide

I created a decision matrix to help you choose:

Model Selection Decision Matrix
YOUR PRIORITY              RECOMMENDED MODEL           WHY
-------------------------  -------------------------   ---------------------------
Best overall quality       Mistral Small 24B ablit.    Highest quality/size ratio
Maximum uncensorship       GLM-4.7-Flash Heretic       Heretic-scale abliteration
Multilingual support       Qwen 2.5/3.5 9B ablit.      100+ languages trained
Long documents             Mistral NeMo 12B ablit.     Most VRAM for context
Complex reasoning          DeepSeek-R1 7B/8B ablit.    R1 reasoning in small pkg
Conservative VRAM use      Qwen 9B or Mistral NeMo     6-8GB usage leaves headroom

The Bottom Line

For 16GB VRAM users seeking uncensored LLM models in 2026:

Start here:

  1. Mistral Small 24B abliterated - Best overall balance of uncensorship and quality
  2. Q4_K_M quantization - Optimal for 16GB VRAM
  3. Ollama or LM Studio - Easiest deployment path

Specialize based on needs:

  • Maximum uncensorship: GLM-4.7-Flash Heretic
  • Multilingual: Qwen 9B abliterated
  • Long context: Mistral NeMo 12B abliterated
  • Reasoning: DeepSeek-R1 7B abliterated

Find abliterated models on HuggingFace using keywords: “heretic”, “abliterated”, “uncensored”, or check trusted collections from failspy and cognitivecomputations.

The key insight from my testing: Mistral models naturally have less restrictive training, making them the best foundation for uncensored variants. Abliteration works on any model, but starting with a less-restrictive base yields better results.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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