What are the hardware requirements for Qwen3.8 27B W4A16 AutoRound GPTQ?

Qwen3.8 27B W4A16 AutoRound GPTQ requires approximately 19.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.8 27B W4A16 AutoRound GPTQ and how large is it?

Qwen3.8 27B W4A16 AutoRound GPTQ is developed by Vishva007, 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 W4A16 AutoRound GPTQ?

Qwen3.8 27B W4A16 AutoRound GPTQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.8 27B W4A16 AutoRound GPTQ by Vishva007 — VRAM 19.2GB

Name: Qwen3.8 27B W4A16 AutoRound GPTQ
Author: Vishva007

Qwen3.8 27B W4A16 AutoRound GPTQ is an open-source language model by Vishva007. Features: 27b LLM, VRAM: 19.2GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.37.

4-bit 4bit Auto-round Base model:quantized:qwen/qwen... Base model:qwen/qwen3.8-27b Conversational Gptq Image-text-to-text Quantization Quantized Qwen3 5 Region:us Safetensors Sharded Tensorflow Vlm

Model Card on HF 🤗: https://huggingface.co/Vishva007/Qwen3.8-27B-W4A16-AutoRound-GPTQ

Qwen3.8 27B W4A16 AutoRound GPTQ Parameters and Internals

LLM Name	Qwen3.8 27B W4A16 AutoRound GPTQ
Repository 🤗	https://huggingface.co/Vishva007/Qwen3.8-27B-W4A16-AutoRound-GPTQ
Base Model(s)	Qwen/Qwen3.8-27B Qwen/Qwen3.8-27B
Model Size	27b
Required VRAM	19.2 GB
Updated	2026-08-16
Maintainer	Vishva007
Model Type	qwen3_5
Model Files	2.5 GB: 1-of-5 5.0 GB: 2-of-5 5.0 GB: 3-of-5 5.0 GB: 4-of-5 1.7 GB: 5-of-5 0.8 GB
GPTQ Quantization	Yes
Quantization Type	gptq
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	5.15.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Torch Data Type	float16
Errors	replace

Rank the Qwen3.8 27B W4A16 AutoRound GPTQ 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 W4A16 AutoRound GPTQ

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3.8 27B GPTQ 4bit	0K / 21 GB	2433	5
Qwen3.6 27B GPTQ Pro 4bit	0K / 18.8 GB	415407	39
Qwen3.5 27B GPTQ Int4	0K / 30.1 GB	105381	57
... PristinelyUncensored GPTQ MTP	0K / 19.6 GB	12	1
...s3.5 27B V3 PolarQuant V7 GPTQ	0K / 19.2 GB	302	3
Qwopus3.5 27B V3 HLWQ V7 GPTQ	0K / 19.2 GB	33	6
Qwen3.5 27B GPTQ Int4	0K / 28.5 GB	24	1
Qwen3.6 27B OptiQ 4bit	256K / 18.7 GB	13065	59
Qwen3.8 27B 8bit	0K / 29.4 GB	13892	33
Bonsai 27B Mlx 1bit	0K / 5.1 GB	809309	205

Note: green Score (e.g. "73.2") means that the model is better than Vishva007/Qwen3.8-27B-W4A16-AutoRound-GPTQ.