What are the hardware requirements for Qwen3 30B A6B 16 Extreme?

Qwen3 30B A6B 16 Extreme requires approximately 61.1 GB of VRAM and supports a context window of 40K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3 30B A6B 16 Extreme and how large is it?

Qwen3 30B A6B 16 Extreme is developed by DavidAU, a model with 30b 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 30B A6B 16 Extreme?

Qwen3 30B A6B 16 Extreme is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3 30B A6B 16 Extreme by DavidAU — VRAM 61.1GB, 40K context

Name: Qwen3 30B A6B 16 Extreme
Author: DavidAU

Qwen3 30B A6B 16 Extreme is an open-source language model by DavidAU. Features: 30b LLM, VRAM: 61.1GB, Context: 40K, LLM Explorer Score: 0.19.

16 experts 32 k context Base model:finetune:qwen/qwen3... Base model:qwen/qwen3-30b-a3b-... Conversational Endpoints compatible Qwen3 Qwen3 moe Reasoning Region:us Safetensors Sharded Tensorflow Thinking

Model Card on HF 🤗: https://huggingface.co/DavidAU/Qwen3-30B-A6B-16-Extreme

Qwen3 30B A6B 16 Extreme Parameters and Internals

LLM Name	Qwen3 30B A6B 16 Extreme
Repository 🤗	https://huggingface.co/DavidAU/Qwen3-30B-A6B-16-Extreme
Base Model(s)	Qwen3 30B A3B Base Qwen/Qwen3-30B-A3B-Base
Model Size	30b
Required VRAM	61.1 GB
Updated	2026-08-11
Maintainer	DavidAU
Model Type	qwen3_moe
Model Files	4.0 GB: 1-of-16 4.0 GB: 2-of-16 4.0 GB: 3-of-16 4.0 GB: 4-of-16 4.0 GB: 5-of-16 4.0 GB: 6-of-16 4.0 GB: 7-of-16 4.0 GB: 8-of-16 4.0 GB: 9-of-16 4.0 GB: 10-of-16 4.0 GB: 11-of-16 4.0 GB: 12-of-16 4.0 GB: 13-of-16 4.0 GB: 14-of-16 4.0 GB: 15-of-16 1.1 GB: 16-of-16
Model Architecture	Qwen3MoeForCausalLM
Context Length	40960
Model Max Length	40960
Transformers Version	4.51.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen3 30B A6B 16 Extreme 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 30B A6B 16 Extreme

Best Alternatives	Context / RAM	Downloads	Likes
...LL MASTER CODER M 1million Ctx	1024K / 84.8 GB	108	9
... 42B A3B Thinking Total Recall	986K / 84.8 GB	26	0
Qwen3 30B A3B YOYO V3	986K / 61.1 GB	11	7
...07 YOYO2 TOTAL RECALL Instruct	986K / 84.8 GB	27	1
...07 YOYO2 TOTAL RECALL Instruct	986K / 106 GB	20	0
Qwen3 30B A3B YOYO V2	986K / 61.1 GB	19	4
Qwen3 Coder 30B A3B Instruct	256K / 61.1 GB	1746771	1168
Qwen3 30B A3B Instruct 2507	256K / 61.1 GB	3937	13
Qwen3 30B A3B Instruct 2507	256K / 61.1 GB	1903736	824
...en3 Coder 30B A3B Instruct FP8	256K / 31.2 GB	1556690	191

Note: green Score (e.g. "73.2") means that the model is better than DavidAU/Qwen3-30B-A6B-16-Extreme.