What are the hardware requirements for Qwen3.8 Flash Next APEX GGUF?

Qwen3.8 Flash Next APEX GGUF requires approximately 0.5 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.8 Flash Next APEX GGUF and how large is it?

Qwen3.8 Flash Next APEX GGUF is developed by Atomic-Germ. 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 Flash Next APEX GGUF?

Qwen3.8 Flash Next APEX GGUF is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.8 Flash Next APEX GGUF by Atomic-Germ — VRAM 0.5GB

Name: Qwen3.8 Flash Next APEX GGUF
Author: Atomic-Germ

Qwen3.8 Flash Next APEX GGUF is an open-source language model by Atomic-Germ. Features: LLM, VRAM: 0.5GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.31.

4-bit Apex Base model:mudler/qwen3.8-flas... Base model:quantized:mudler/qw... Conversational Endpoints compatible Gguf Guanaco Image-text-to-text Imatrix Moe Nvme streaming Q4 Q4 0 Quantized Qwen Qwen3.8 Qwen4 exp Region:us

Model Card on HF 🤗: https://huggingface.co/Atomic-Germ/Qwen3.8-Flash-Next-APEX-GGUF

Qwen3.8 Flash Next APEX GGUF Parameters and Internals

LLM Name	Qwen3.8 Flash Next APEX GGUF
Repository 🤗	https://huggingface.co/Atomic-Germ/Qwen3.8-Flash-Next-APEX-GGUF
Base Model(s)	mudler/Qwen3.8-Flash-Next-APEX-GGUF mudler/Qwen3.8-Flash-Next-APEX-GGUF
Required VRAM	0.5 GB
Updated	2026-10-21
Maintainer	Atomic-Germ
Model Type	qwen4_exp
Model Files	0.5 GB: 1-of-5 28.8 GB: 2-of-5 14.9 GB: 3-of-5 15.0 GB: 4-of-5 13.8 GB: 5-of-5 0.5 GB: 1-of-6 28.8 GB: 2-of-6 14.6 GB: 3-of-6 14.6 GB: 4-of-6 14.6 GB: 5-of-6 11.9 GB: 6-of-6 0.6 GB 0.9 GB
GGUF Quantization	Yes
Quantization Type	gguf\|q4\|q4_0
Model Architecture	Qwen4ExpForConditionalGeneration
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 Flash Next APEX GGUF 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 Flash Next APEX GGUF

Best Alternatives	Context / RAM	Downloads	Likes
...wen3.8 Flash Next Coder Cut240	0K / 4.5 GB	4622	2
....8 Flash Next Mixed Quant GGUF	0K / GB	2325	3
....8 Flash Next Mixed Quant GGUF	0K / GB	482	1
... Flash Next Abliterated V2 HGN	0K / GB	36	2
Qwen3.8 Flash Next MLX Q9	0K / 200.6 GB	3888	1
Qwen3.8 Flash Next MLX Q4	0K / 104.3 GB	3382	1
...kingCap Qwen3.8 Flash Next HGN	0K / GB	592	1
...xt Heretic ReverseEngineer HGN	0K / GB	853	2
...gCap Qwen3.8 Flash Next Strixw	0K / GB	313	1
...Qwen3.8 Flash Next Heretic HGN	0K / GB	313	1

Note: green Score (e.g. "73.2") means that the model is better than Atomic-Germ/Qwen3.8-Flash-Next-APEX-GGUF.