What are the hardware requirements for Qwen3.8 27B AutoRound W4A16 G128?

Qwen3.8 27B AutoRound W4A16 G128 requires approximately 8.8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.8 27B AutoRound W4A16 G128 and how large is it?

Qwen3.8 27B AutoRound W4A16 G128 is developed by weddle, 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 AutoRound W4A16 G128?

Qwen3.8 27B AutoRound W4A16 G128 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.8 27B AutoRound W4A16 G128 by weddle — VRAM 8.8GB

Name: Qwen3.8 27B AutoRound W4A16 G128
Author: weddle

Qwen3.8 27B AutoRound W4A16 G128 is an open-source language model by weddle. Features: 27b LLM, VRAM: 8.8GB, License: apache-2.0.

4-bit Auto-round Base model:quantized:qwen/qwen... Base model:qwen/qwen3.8-27b Conversational Endpoints compatible G128 Image-text-to-text Int4 Intel-xpu Qwen3 5 Region:us Safetensors Sharded Tensorflow W4a16

Model Card on HF 🤗: https://huggingface.co/weddle/Qwen3.8-27B-AutoRound-W4A16-G128

Qwen3.8 27B AutoRound W4A16 G128 Parameters and Internals

LLM Name	Qwen3.8 27B AutoRound W4A16 G128
Repository 🤗	https://huggingface.co/weddle/Qwen3.8-27B-AutoRound-W4A16-G128
Base Model(s)	Qwen/Qwen3.8-27B Qwen/Qwen3.8-27B
Model Size	27b
Required VRAM	8.8 GB
Updated	2026-09-13
Maintainer	weddle
Model Type	qwen3_5
Model Files	0.2 GB: 2-of-67 0.2 GB: 3-of-67 0.2 GB: 4-of-67 0.2 GB: 5-of-67 0.2 GB: 6-of-67 0.2 GB: 7-of-67 0.2 GB: 8-of-67 0.2 GB: 9-of-67 0.2 GB: 10-of-67 0.2 GB: 11-of-67 0.2 GB: 12-of-67 0.2 GB: 13-of-67 0.2 GB: 14-of-67 0.2 GB: 15-of-67 0.2 GB: 16-of-67 0.2 GB: 17-of-67 0.2 GB: 18-of-67 0.2 GB: 19-of-67 0.2 GB: 20-of-67 0.2 GB: 21-of-67 0.2 GB: 22-of-67 0.2 GB: 23-of-67 0.2 GB: 24-of-67 0.2 GB: 25-of-67 0.2 GB: 26-of-67 0.2 GB: 27-of-67 0.2 GB: 28-of-67 0.2 GB: 29-of-67 0.2 GB: 30-of-67 0.2 GB: 31-of-67 0.2 GB: 32-of-67 0.2 GB: 33-of-67 0.2 GB: 34-of-67 0.2 GB: 35-of-67 0.2 GB: 36-of-67 0.2 GB: 37-of-67 0.2 GB: 38-of-67 0.2 GB: 39-of-67 0.2 GB: 40-of-67 0.2 GB: 41-of-67 0.2 GB: 42-of-67 0.2 GB: 43-of-67 0.2 GB: 44-of-67 0.2 GB: 45-of-67
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Transformers Version	5.8.0

Rank the Qwen3.8 27B AutoRound W4A16 G128 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 AutoRound W4A16 G128

Best Alternatives	Context / RAM	Downloads	Likes
Agens Pilot	1024K / 54.7 GB	132	1
...wopus3.8 27B Flash Mlx OQ4e 1M	1024K / 16.9 GB	211	1
Qwen3.6 27B	0K / 55.4 GB	6728138	2197
Qwen3.8 27B MLX	0K / 16 GB	1411	18
Qwen3.8 27B	0K / 55.6 GB	2727	21
Qwen3.8 27B FP8	0K / 0.4 GB	123157	416
Qwen3.8 27B	0K / 55.6 GB	2	8753
Qwen3.6 27B NVFP4	0K / 22 GB	1887525	419
Qwen3.8 27B NVFP4	0K / 22 GB	19195	74
Qwen3.6 27B FP8	0K / 0.4 GB	7740472	342

Note: green Score (e.g. "73.2") means that the model is better than weddle/Qwen3.8-27B-AutoRound-W4A16-G128.