What are the hardware requirements for Qwen3.6 27B OBLITERATED?

Qwen3.6 27B OBLITERATED requires approximately 53.6 GB of VRAM and supports a context window of 256K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.6 27B OBLITERATED and how large is it?

Qwen3.6 27B OBLITERATED is developed by OBLITERATUS, 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 Qwen3.6 27B OBLITERATED?

Qwen3.6 27B OBLITERATED is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.6 27B OBLITERATED by OBLITERATUS — VRAM 53.6GB, 256K context

Name: Qwen3.6 27B OBLITERATED
Author: OBLITERATUS

Qwen3.6 27B OBLITERATED is an open-source language model by OBLITERATUS. Features: 27b LLM, VRAM: 53.6GB, Context: 256K, License: apache-2.0, LLM Explorer Score: 0.33.

Base model:quantized:qwen/qwen... Base model:qwen/qwen3.6-27b Conversational Endpoints compatible Gguf Llama.cpp Lm-studio Obliteratus Ollama Qwen Qwen3 Qwen3.6 Qwen3 5 text Red-team Refusal-analysis Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/OBLITERATUS/Qwen3.6-27B-OBLITERATED

Qwen3.6 27B OBLITERATED Parameters and Internals

LLM Name	Qwen3.6 27B OBLITERATED
Repository 🤗	https://huggingface.co/OBLITERATUS/Qwen3.6-27B-OBLITERATED
Base Model(s)	Qwen/Qwen3.6-27B Qwen/Qwen3.6-27B
Model Size	27b
Required VRAM	53.6 GB
Updated	2026-07-22
Maintainer	OBLITERATUS
Model Type	qwen3_5_text
Model Files	2.5 GB: 1-of-28 1.9 GB: 2-of-28 1.9 GB: 3-of-28 1.9 GB: 4-of-28 1.9 GB: 5-of-28 1.9 GB: 6-of-28 1.9 GB: 7-of-28 1.9 GB: 8-of-28 1.9 GB: 9-of-28 1.9 GB: 10-of-28 1.9 GB: 11-of-28 1.9 GB: 12-of-28 1.9 GB: 13-of-28 1.9 GB: 14-of-28 1.9 GB: 15-of-28 1.9 GB: 16-of-28 1.9 GB: 17-of-28 1.9 GB: 18-of-28 1.9 GB: 19-of-28 1.9 GB: 20-of-28 1.9 GB: 21-of-28 1.9 GB: 22-of-28 1.9 GB: 23-of-28 1.9 GB: 24-of-28 1.9 GB: 25-of-28 1.9 GB: 26-of-28 2.5 GB: 27-of-28 1.1 GB: 28-of-28
Model Architecture	Qwen3_5ForCausalLM
License	apache-2.0
Context Length	262144
Model Max Length	262144
Transformers Version	5.8.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	248320
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen3.6 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

Quantized Models of the Qwen3.6 27B OBLITERATED

Model	Likes	Downloads	VRAM
...en3.6 27B OBLITERATED MLX 4bit	3	646	15 GB

Best Alternatives to Qwen3.6 27B OBLITERATED

Best Alternatives	Context / RAM	Downloads	Likes
Hemmingway 1	256K / 53.8 GB	12371	880
Hemmingway 1	256K / 53.8 GB	9089	888
JEV 27B	256K / 53.9 GB	178643	79
OrcaSAQ 2 27B	256K / 12.3 GB	1330	169
Darwin 27B RSI	256K / 53.8 GB	459	14
Solar JEV 27B NVFP4 FP8	256K / 29.2 GB	41	1
RAG Gate 27B	256K / 53.8 GB	243	0
Mnma Qwen3.8 27b Nvfp4	256K / 18.8 GB	527	11
Solar JEV 27B R32	256K / 54.6 GB	53	0
...2 Abliterated OptiQ 3.7bpw Mlx	256K / 13.4 GB	6727	14

Note: green Score (e.g. "73.2") means that the model is better than OBLITERATUS/Qwen3.6-27B-OBLITERATED.