What are the hardware requirements for Qwen3.8 27B OBLITERATED AWQ W4A16?

Qwen3.8 27B OBLITERATED AWQ W4A16 requires approximately 0.8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.8 27B OBLITERATED AWQ W4A16 and how large is it?

Qwen3.8 27B OBLITERATED AWQ W4A16 is developed by MurtazaNasir, 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 AWQ W4A16?

Qwen3.8 27B OBLITERATED AWQ W4A16 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 AWQ W4A16 by MurtazaNasir — VRAM 0.8GB

Name: Qwen3.8 27B OBLITERATED AWQ W4A16
Author: MurtazaNasir

Qwen3.8 27B OBLITERATED AWQ W4A16 is an open-source language model by MurtazaNasir. Features: 27b LLM, VRAM: 0.8GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.26.

Abliterated Ai-safety-research Awq Base model:obliteratus/qwen3.8... Base model:quantized:obliterat... Compressed-tensors Conversational Int4 Obliteratus Quantized Qwen3 Qwen3.8 Qwen3 5 Red-team Region:us Safetensors Uncensored Vllm W4a16

Model Card on HF 🤗: https://huggingface.co/MurtazaNasir/Qwen3.8-27B-OBLITERATED-AWQ-W4A16

Qwen3.8 27B OBLITERATED AWQ W4A16 Parameters and Internals

LLM Name	Qwen3.8 27B OBLITERATED AWQ W4A16
Repository 🤗	https://huggingface.co/MurtazaNasir/Qwen3.8-27B-OBLITERATED-AWQ-W4A16
Base Model(s)	OBLITERATUS/Qwen3.8-27B-OBLITERATED OBLITERATUS/Qwen3.8-27B-OBLITERATED
Model Size	27b
Required VRAM	0.8 GB
Updated	2026-08-25
Maintainer	MurtazaNasir
Model Type	qwen3_5
Model Files	0.8 GB 18.7 GB
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	5.14.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Qwen3.8 27B OBLITERATED AWQ W4A16 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 AWQ W4A16

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3.6 27B AWQ INT4	0K / 20.4 GB	2563390	103
Qwen3.6 27B AWQ 4bit	0K / 18.7 GB	260342	3
Qwen3.6 27B AWQ	0K / 22 GB	668924	24
Qwen3.6 27B AWQ BF16 INT4	0K / 28.3 GB	419171	41
Qwen3.8 27B AWQ INT4	0K / 21 GB	21015	32
...wen3.6 27B Abliterated AWQ MTP	0K / 18.4 GB	244978	12
Qwen3.5 27B AWQ	0K / 21.9 GB	370920	44
Qwen3.8 27B AWQ W4A16 ASYM	0K / 0.8 GB	23216	4
Qwen3.5 27B AWQ 4bit	0K / 20 GB	279064	42
Qwen3.8 27B AWQ 4bit	0K / 27.7 GB	11874	6

Note: green Score (e.g. "73.2") means that the model is better than MurtazaNasir/Qwen3.8-27B-OBLITERATED-AWQ-W4A16.