What are the hardware requirements for Qwen3.8 27B MXFP4 MagicQuant GGUF?

Qwen3.8 27B MXFP4 MagicQuant GGUF requires approximately 0.6 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.8 27B MXFP4 MagicQuant GGUF and how large is it?

Qwen3.8 27B MXFP4 MagicQuant GGUF is developed by magiccodingman, 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 MXFP4 MagicQuant GGUF?

Qwen3.8 27B MXFP4 MagicQuant GGUF is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.8 27B MXFP4 MagicQuant GGUF by magiccodingman — VRAM 0.6GB

Name: Qwen3.8 27B MXFP4 MagicQuant GGUF
Author: magiccodingman

Qwen3.8 27B MXFP4 MagicQuant GGUF is an open-source language model by magiccodingman. Features: 27b LLM, VRAM: 0.6GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.35.

Awq Base model:amd/qwen3.8-27b-qua... Base model:quantized:amd/qwen3... Conversational Endpoints compatible Gguf Image-text-to-text Imatrix Magicquant Mtp Mxfp4 Q2 Quantized Quark Qwen3 5 Region:us

Model Card on HF 🤗: https://huggingface.co/magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF

Qwen3.8 27B MXFP4 MagicQuant GGUF Parameters and Internals

LLM Name	Qwen3.8 27B MXFP4 MagicQuant GGUF
Repository 🤗	https://huggingface.co/magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF
Base Model(s)	amd/Qwen3.8-27B-Quark-AWQ-MXFP4 amd/Qwen3.8-27B-Quark-AWQ-MXFP4
Model Size	27b
Required VRAM	0.6 GB
Updated	2026-08-30
Maintainer	magiccodingman
Model Type	qwen3_5
Model Files	18.9 GB 11.9 GB 10.7 GB 8.2 GB 15.0 GB 12.0 GB 10.9 GB 13.9 GB 9.8 GB 13.0 GB 14.5 GB 0.0 GB 0.9 GB 0.6 GB
GGUF Quantization	Yes
AWQ Quantization	Yes
Quantization Type	gguf\|awq\|q2\|q4_k
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	5.2.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Qwen3.8 27B MXFP4 MagicQuant 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 Qwen3.8 27B MXFP4 MagicQuant GGUF

Best Alternatives	Context / RAM	Downloads	Likes
...N V1.1 NM DAU NEO MAX MTP GGUF	0K / 0.9 GB	272422	273
Qwen3.6 27B AWQ INT4	0K / 20.4 GB	2563390	103
Qwen3.8 27B AWQ INT4	0K / 21 GB	21015	32
Qwen3.8 27B GGUF	0K / 0.9 GB	0	685
... Opus Reasoning Distilled GGUF	0K / 0.9 GB	67921	679
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 4bit	0K / 27.7 GB	11874	6
Qwen3.8 27B AWQ 5.0bpw	0K / 17.3 GB	1445	20
...wen3.6 27B Abliterated AWQ MTP	0K / 18.4 GB	244978	12

Note: green Score (e.g. "73.2") means that the model is better than magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF.