What are the hardware requirements for Qwen3.8 27B MXPF4 CT AutoRound?

Qwen3.8 27B MXPF4 CT AutoRound requires approximately 20.3 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.8 27B MXPF4 CT AutoRound and how large is it?

Qwen3.8 27B MXPF4 CT AutoRound is developed by INCModel2, 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 MXPF4 CT AutoRound?

Qwen3.8 27B MXPF4 CT 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 MXPF4 CT AutoRound by INCModel2 — VRAM 20.3GB

Name: Qwen3.8 27B MXPF4 CT AutoRound
Author: INCModel2

Qwen3.8 27B MXPF4 CT AutoRound is an open-source language model by INCModel2. Features: 27b LLM, VRAM: 20.3GB, License: apache-2.0, LLM Explorer Score: 0.3.

Arxiv:2309.05516 8-bit Compressed-tensors Conversational Endpoints compatible Image-text-to-text Qwen3 5 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/INCModel2/Qwen3.8-27B-MXPF4-CT-AutoRound

Qwen3.8 27B MXPF4 CT AutoRound Parameters and Internals

LLM Name	Qwen3.8 27B MXPF4 CT AutoRound
Repository 🤗	https://huggingface.co/INCModel2/Qwen3.8-27B-MXPF4-CT-AutoRound
Model Size	27b
Required VRAM	20.3 GB
Updated	2026-08-18
Maintainer	INCModel2
Model Type	qwen3_5
Model Files	3.4 GB: 1-of-18 2.5 GB: 2-of-18 1.7 GB: 3-of-18 1.1 GB: 4-of-18 0.6 GB: 5-of-18 1.1 GB: 6-of-18 0.6 GB: 7-of-18 1.1 GB: 8-of-18 0.6 GB: 9-of-18 1.1 GB: 10-of-18 0.6 GB: 11-of-18 0.8 GB: 12-of-18 1.1 GB: 13-of-18 0.6 GB: 14-of-18 1.1 GB: 15-of-18 0.6 GB: 16-of-18 1.1 GB: 17-of-18 0.6 GB: 18-of-18
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	5.8.0.dev0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Qwen3.8 27B MXPF4 CT 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 MXPF4 CT AutoRound

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3.8 27B FP8	0K / 0.4 GB	123157	416
Qwen3.6 27B	0K / 55.4 GB	6728138	2197
Qwen3.8 27B	0K / 55.6 GB	2	8753
Qwen3.6 27B NVFP4	0K / 22 GB	1887525	419
Huihui Qwen3.8 27B Abliterated	0K / 55.6 GB	1485	76
ThinkingCap Qwen3.6 27B	0K / 54.7 GB	35947	614
Qwen3.6 27B FP8	0K / 0.4 GB	7740472	342
...n3.8 27B MTPLX Optimized Speed	0K / 19.6 GB	6989	50
Qwen3.5 27B	0K / 55.2 GB	2536294	1028
Qwen3.5 27B	0K / 55.2 GB	8115	16

Note: green Score (e.g. "73.2") means that the model is better than INCModel2/Qwen3.8-27B-MXPF4-CT-AutoRound.