Best Uncensored AI Models for Your GPU (8GB to 24GB)

A 27-billion-parameter model usually needs about 54 GB of memory. Through ternary quantization, developers squeezed it down to a 5.95 GB file that fits on an 8 GB GPU—with model cards claiming zero refusals on test sets where the original refused 95. Here is a breakdown of how abliteration actually edits model weights, how to calculate your true VRAM footprint once context caches are added, and the top model picks for 8, 12, 16, and 24 GB GPUs. Run these only on your own machine for educational purposes: no refusals means no guardrails, so you own what you ask. What's inside: • The VRAM math: Why file size equals parameters times bits divided by 8, and how ternary quantization drops a 27B model down to 1.8 bits per weight. • What abliteration actually does: How researchers isolate the refusal direction (r-hat) in the residual stream and orthogonalize the weights so the model cannot write it. • Heretic optimization: Using Optuna TPE to co-minimize refusal counts and KL divergence so everyday reasoning does not degrade. • The hidden cache tax: Why enabling Multi-Token Prediction (MTP) and scaling context windows burns up to 30,000 tokens of memory headroom. • The 8 GB to 24 GB shortlist: Tested picks for every tier, from Ternary Bonsai 2 and Gemma 4 E4B to Qwen 3.8 27B and Qwen 3.6 35B MoE. • The truth about "zero refusals": Why every refusal score is an unstandardized self-test, and why transparent model cards publish their misses. ⏱️ Chapters: 0:00 Squeezing a 27B Model into 5.95 GB 0:16 Mandatory Warning & Educational Scope 0:46 The VRAM Math: How Quantization Squeezes Weights 1:51 Ternary Weights vs. Mixed Precision (GSQ-RCO) 2:51 The Context Cache Penalty (Why VRAM Math Lies) 3:48 Qwen 3.8 Architecture: Gated DeltaNet vs. Attention 4:30 What Abliteration Actually Means (The Refusal Vector) 5:09 The Stick Analogy: Projecting Out Refusals 6:04 Does Removing Refusals Hurt Model IQ? 6:39 Heretic: Automating Abliteration with Optuna 8:07 Why "0 Refusals" Claims Need Extreme Caution 8:44 The MTP Speed Head Trade-Off 10:32 8 GB Tier: Bonsai 2 27B & Gemma 4 E4B 12:06 Older 8 GB Cards: GTX 1070 & 1080 Picks 12:49 12 GB Tier: Bonsai v2 MTP & GSQ-RCO Qwen 14:59 16 GB Tier: Bucoid 27B & Long Context Limits 16:59 Mixture-of-Experts: Gemma 26B A4B & Qwen 35B 17:49 24 GB Tier: Coletti Q4/Q5 & Maximum Quality 20:59 The Golden VRAM Fit Formula 21:42 Final Verdict & Model Pick by GPU Size 23:01 Test Your Own Prompts Responsibly 🔗 Sources: • Refusal Direction Research Paper (Arditi et al.): https://arxiv.org/html/2406.11717 • Heretic Automated Abliteration: https://github.com/p-e-w/heretic • Maxime Labonne Abliteration Guide: https://huggingface.co/blog/mlabonne/... • Ternary Bonsai 2 27B Heretic (OS-Software): https://huggingface.co/OS-Software/Te... • Jonathan Coletti Qwen 3.8 27B Uncensored: https://huggingface.co/JonathanColett... • HauhauCS Qwen 3.8 27B Aggressive MTP: https://huggingface.co/HauhauCS/Qwen3... • Huihui-AI Qwen 3.8 27B Abliterated: https://huggingface.co/huihui-ai/Huih... • 0bserverx Qwen 3.8 27B Heretic GSQ-RCO: https://huggingface.co/0bserverx/Qwen... • Bucoid Qwen 3.8 27B Heretic Ara: https://huggingface.co/Bucoid/Qwen3.8... • HauhauCS Gemma 4 E4B Aggressive: https://huggingface.co/HauhauCS/Gemma... • HauhauCS Qwen 3.6 35B A3B Aggressive: https://huggingface.co/HauhauCS/Qwen3... • BoldingBuilds Ternary Bonsai 2 v2 MTP: https://huggingface.co/BoldingBuilds/... 🔔 Subscribe:    / @cloud-codes   💙 Become a Member:    / @cloud-codes   🐦 Twitter/X: https://x.com/cloud_codes 💬 Discord:   / discord   #LocalLLM #UncensoredAI #Abliteration #AIHardware #OpenSourceAI #LlamaCPP #CloudCodes