What are the hardware requirements for Qwen3.8 27B INT4 W4A16 AutoRound?

Qwen3.8 27B INT4 W4A16 AutoRound requires approximately 27.9 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.8 27B INT4 W4A16 AutoRound and how large is it?

Qwen3.8 27B INT4 W4A16 AutoRound is developed by goldhub, 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 INT4 W4A16 AutoRound?

Qwen3.8 27B INT4 W4A16 AutoRound is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.8 27B INT4 W4A16 AutoRound by goldhub — VRAM 27.9GB

Name: Qwen3.8 27B INT4 W4A16 AutoRound
Author: goldhub

Qwen3.8 27B INT4 W4A16 AutoRound is an open-source language model by goldhub. Features: 27b LLM, VRAM: 27.9GB, License: apache-2.0, LLM Explorer Score: 0.38.

256k-context 4-bit Auto-round Base model:quantized:qwen/qwen... Base model:qwen/qwen3.8-27b Conversational En Heretic Image-text-to-text Mtp Quantized Qwen Qwen3.8 Qwen3 5 Region:us Ru Safetensors Sharded Tensorflow Video-text-to-text Vision W4a16 Zh

Model Card on HF 🤗: https://huggingface.co/goldhub/Qwen3.8-27B-INT4-W4A16-AutoRound

Qwen3.8 27B INT4 W4A16 AutoRound Parameters and Internals

LLM Name	Qwen3.8 27B INT4 W4A16 AutoRound
Repository 🤗	https://huggingface.co/goldhub/Qwen3.8-27B-INT4-W4A16-AutoRound
Base Model(s)	Qwen/Qwen3.8-27B Qwen/Qwen3.8-27B
Model Size	27b
Required VRAM	27.9 GB
Updated	2026-08-15
Maintainer	goldhub
Model Type	qwen3_5
Model Files	3.2 GB: 1-of-9 3.1 GB: 2-of-9 3.2 GB: 3-of-9 3.2 GB: 4-of-9 3.2 GB: 5-of-9 3.2 GB: 6-of-9 3.2 GB: 7-of-9 3.1 GB: 8-of-9 2.5 GB: 9-of-9 0.3 GB
Supported Languages	en zh ru
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 INT4 W4A16 AutoRound 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 INT4 W4A16 AutoRound

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3.8 27B FP8	0K / 0.4 GB	123157	416
Qwen3.8 27B	0K / 55.6 GB	2	8753
Qwen3.6 27B	0K / 55.4 GB	6728138	2197
Qwen3.6 27B NVFP4	0K / 22 GB	1887525	419
Qwen3.8 27B NVFP4	0K / 22 GB	30122	14
ThinkingCap Qwen3.6 27B	0K / 54.7 GB	35947	614
Qwen3.8 27B INT8 W8A16 MTP	0K / 30.7 GB	4723	26
Qwen3.6 27B FP8	0K / 0.4 GB	7740472	342
Qwen3.8 27B Heretic Ara	0K / 54.6 GB	841	46
Qwen3.5 27B	0K / 55.2 GB	2536294	1028

Note: green Score (e.g. "73.2") means that the model is better than goldhub/Qwen3.8-27B-INT4-W4A16-AutoRound.