What are the hardware requirements for Qwen3.8 27B AutoQuant NVFP4 FP8 5bit?

Qwen3.8 27B AutoQuant NVFP4 FP8 5bit requires approximately 20.4 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.8 27B AutoQuant NVFP4 FP8 5bit and how large is it?

Qwen3.8 27B AutoQuant NVFP4 FP8 5bit is developed by acyildirimer, 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 AutoQuant NVFP4 FP8 5bit?

Qwen3.8 27B AutoQuant NVFP4 FP8 5bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.8 27B AutoQuant NVFP4 FP8 5bit by acyildirimer — VRAM 20.4GB

Name: Qwen3.8 27B AutoQuant NVFP4 FP8 5bit
Author: acyildirimer

Qwen3.8 27B AutoQuant NVFP4 FP8 5bit is an open-source language model by acyildirimer. Features: 27b LLM, VRAM: 20.4GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.31.

5bit 8-bit Autoquant Base model:quantized:qwen/qwen... Base model:qwen/qwen3.8-27b Conversational Endpoints compatible Fp8 Image-text-to-text Mixed-precision Modelopt Nvfp4 Quantized Qwen3.8 Qwen3 5 Region:us Safetensors Sharded Tensorflow Vllm

Model Card on HF 🤗: https://huggingface.co/acyildirimer/Qwen3.8-27B-AutoQuant-NVFP4-FP8-5bit

Qwen3.8 27B AutoQuant NVFP4 FP8 5bit Parameters and Internals

LLM Name	Qwen3.8 27B AutoQuant NVFP4 FP8 5bit
Repository 🤗	https://huggingface.co/acyildirimer/Qwen3.8-27B-AutoQuant-NVFP4-FP8-5bit
Base Model(s)	Qwen/Qwen3.8-27B Qwen/Qwen3.8-27B
Model Size	27b
Required VRAM	20.4 GB
Updated	2026-09-01
Maintainer	acyildirimer
Model Type	qwen3_5
Model Files	10.0 GB: 1-of-3 10.2 GB: 2-of-3 0.2 GB: 3-of-3
Quantization Type	5bit
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	5.9.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Qwen3.8 27B AutoQuant NVFP4 FP8 5bit 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 AutoQuant NVFP4 FP8 5bit

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3.6 27B OptiQ 4bit	256K / 18.7 GB	13065	59
Bonsai 27B Mlx 1bit	0K / 5.1 GB	809309	205
Ternary Bonsai 27B Mlx 2bit	0K / 8.5 GB	809024	168
Qwen3.8 27B 8bit	0K / 29.4 GB	13892	33
...en3.8 27B Abliterated MLX 4bit	0K / 16 GB	1664	26
Qwen3.6 27B MLX 8bit	0K / 29.4 GB	879145	14
Qwen3.6 27B MLX 6bit	0K / 22.7 GB	812977	2
Qwen3.8 27B 4bit	0K / 16 GB	6055	27
Qwen3.8 27B MLX 4bit	0K / 16 GB	19712	10
Qwen3.8 27B MLX 8bit	0K / 29.4 GB	24811	7

Note: green Score (e.g. "73.2") means that the model is better than acyildirimer/Qwen3.8-27B-AutoQuant-NVFP4-FP8-5bit.