What are the hardware requirements for Qwen3.6 27B Claude Opus Reasoning Distilled GGUF?

Qwen3.6 27B Claude Opus Reasoning Distilled GGUF requires approximately 10.7 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.6 27B Claude Opus Reasoning Distilled GGUF and how large is it?

Qwen3.6 27B Claude Opus Reasoning Distilled GGUF is developed by rico03, 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 Claude Opus Reasoning Distilled GGUF?

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

Qwen3.6 27B Claude Opus Reasoning Distilled GGUF by rico03 — VRAM 10.7GB

Name: Qwen3.6 27B Claude Opus Reasoning Distilled GGUF
Author: rico03

Qwen3.6 27B Claude Opus Reasoning Distilled GGUF is an open-source language model by rico03. Features: 27b LLM, VRAM: 10.7GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.31.

Base model:quantized:qwen/qwen... Base model:qwen/qwen3.6-27b Claude-opus Conversational Dataset:jackrong/qwen3.5-reaso... Dataset:nohurry/opus-4.6-reaso... Dataset:roman1111111/claude-op... Distillation En Endpoints compatible Finetuned Gguf Llama-cpp Multilingual Ollama Q2 Quantized Qwen3.6 Qwen3 5 Reasoning Region:us

Model Card on HF 🤗: https://huggingface.co/rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF

Qwen3.6 27B Claude Opus Reasoning Distilled GGUF Parameters and Internals

LLM Name	Qwen3.6 27B Claude Opus Reasoning Distilled GGUF
Repository 🤗	https://huggingface.co/rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF
Model Name	unsloth/Qwen3.6-27B
Base Model(s)	Qwen/Qwen3.6-27B Qwen/Qwen3.6-27B
Model Size	27b
Required VRAM	10.7 GB
Updated	2026-07-10
Maintainer	rico03
Model Type	qwen3_5
Model Files	10.7 GB 13.3 GB 16.5 GB 15.6 GB 19.2 GB 18.7 GB 22.1 GB 28.6 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf\|q2\|q4_k\|q5_k
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Torch Data Type	bfloat16

Rank the Qwen3.6 27B Claude Opus Reasoning Distilled GGUF 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 Qwen3.6 27B Claude Opus Reasoning Distilled GGUF

Best Alternatives	Context / RAM	Downloads	Likes
...i 27B MLX BF16 Config Repaired	0K / 53.5 GB	678	0
Qwen3.6 27B OptiQ 4bit	256K / 18.7 GB	13065	59
Qwen3.5 27B OptiQ 4bit	0K / 18.6 GB	7269	3
... 711 ReducedRefusal MTPLX 6bit	0K / 21.8 GB	16	2
Qwen3.6 27B UD Q3 K XL Mlx	0K / 18.7 GB	395	3
Apax Q4 K M	0K / 55.2 GB	12	0
MLX Qwopus3.5 27B V3 6bit	0K / 21.8 GB	141	3
... Opus Reasoning Distilled 4bit	0K / 15.1 GB	130	8
Qwen3.5 27B 4bit DWQ	0K / 15.2 GB	59	6
Qwen3.6 27B	0K / 55.4 GB	6728138	2197

Note: green Score (e.g. "73.2") means that the model is better than rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF.