What are the hardware requirements for Qwen3.8 27B OBLITERATED?

Qwen3.8 27B OBLITERATED requires approximately 83.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.8 27B OBLITERATED and how large is it?

Qwen3.8 27B OBLITERATED is developed by geezyforeezy, a model with 27b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Qwen3.8 27B OBLITERATED?

Qwen3.8 27B OBLITERATED is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.8 27B OBLITERATED by geezyforeezy — VRAM 83.2GB

Name: Qwen3.8 27B OBLITERATED
Author: geezyforeezy

Qwen3.8 27B OBLITERATED is an open-source language model by geezyforeezy. Features: 27b LLM, VRAM: 83.2GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.32.

Abliterated Ai-safety-research Base model:quantized:qwen/qwen... Base model:qwen/qwen3.8-27b Conversational Endpoints compatible Gguf Mlx Obliteratus Q2 Quantized Qwen3 Qwen3.8 Qwen3 5 Red-team Region:us Safetensors Sharded Tensorflow Uncensored

Model Card on HF 🤗: https://huggingface.co/geezyforeezy/Qwen3.8-27B-OBLITERATED

Qwen3.8 27B OBLITERATED Parameters and Internals

LLM Name	Qwen3.8 27B OBLITERATED
Repository 🤗	https://huggingface.co/geezyforeezy/Qwen3.8-27B-OBLITERATED
Base Model(s)	Qwen/Qwen3.8-27B Qwen/Qwen3.8-27B
Model Size	27b
Required VRAM	83.2 GB
Updated	2026-10-19
Maintainer	geezyforeezy
Model Type	qwen3_5
Model Files	15.4 GB 10.9 GB 13.5 GB 16.8 GB 19.5 GB 22.4 GB 29.0 GB 0.9 GB 4.0 GB: 1-of-18 2.5 GB: 1-of-28 3.0 GB: 2-of-18 1.9 GB: 2-of-28 2.5 GB: 3-of-18 1.9 GB: 3-of-28 4.0 GB: 4-of-18 1.9 GB: 4-of-28 2.1 GB: 5-of-18 1.9 GB: 5-of-28 4.0 GB: 6-of-18 1.9 GB: 6-of-28 2.1 GB: 7-of-18 1.9 GB: 7-of-28 4.0 GB: 8-of-18 1.9 GB: 8-of-28 2.1 GB: 9-of-18 1.9 GB: 9-of-28 4.0 GB: 10-of-18 1.9 GB: 10-of-28 2.1 GB: 11-of-18 1.9 GB: 11-of-28 4.0 GB: 12-of-18 1.9 GB: 12-of-28 2.1 GB: 13-of-18 1.9 GB: 13-of-28 4.0 GB: 14-of-18 1.9 GB: 14-of-28 2.1 GB: 15-of-18 1.9 GB: 15-of-28 4.0 GB: 16-of-18 1.9 GB: 16-of-28 2.1 GB: 17-of-18
GGUF Quantization	Yes
Quantization Type	q2\|gguf\|q4_k\|q5_k
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Transformers Version	5.8.0.dev0

Rank the Qwen3.8 27B OBLITERATED capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Qwen3.8 27B OBLITERATED

Best Alternatives	Context / RAM	Downloads	Likes
...N V1.1 NM DAU NEO MAX MTP GGUF	0K / 0.9 GB	421538	328
... Opus Reasoning Distilled GGUF	0K / 0.9 GB	67921	679
Qwen3.8 27B GGUF	0K / 0.9 GB	0	685
...B Abliterated V2 PQ2 0 MTP MLX	0K / 0.8 GB	2770	7
...inkingCap Qwen3.8 27B MIX GGUF	0K / 13.4 GB	1395	7
Swift Qwen3.8 27B MIX GGUF	0K / 0.9 GB	2219	2
...N Blackwell DFlash2 Ultra V1.0	0K / 0.9 GB	3531	3
Qwen3.8 27B Taiwan Pilled	0K / 55.6 GB	499	1
EleMo V3 Base	0K / 0.9 GB	1989	1
Ternary Bonsai 2 27B MLX 4bit	0K / 16 GB	1175	2

Note: green Score (e.g. "73.2") means that the model is better than geezyforeezy/Qwen3.8-27B-OBLITERATED.