What are the hardware requirements for Qwen3 30B A3B Thinking 2507 AWQ?

Qwen3 30B A3B Thinking 2507 AWQ requires approximately 16.7 GB of VRAM and supports a context window of 256K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3 30B A3B Thinking 2507 AWQ and how large is it?

Qwen3 30B A3B Thinking 2507 AWQ is developed by cpatonn, 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 A3B Thinking 2507 AWQ?

Qwen3 30B A3B Thinking 2507 AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3 30B A3B Thinking 2507 AWQ by cpatonn — VRAM 16.7GB, 256K context

Name: Qwen3 30B A3B Thinking 2507 AWQ
Author: cpatonn

Qwen3 30B A3B Thinking 2507 AWQ is an open-source language model by cpatonn. Features: 30b LLM, VRAM: 16.7GB, Context: 256K, License: apache-2.0, Quantized, LLM Explorer Score: 0.23.

Arxiv:2505.09388 Autotrain compatible Awq Base model:quantized:qwen/qwen... Base model:qwen/qwen3-30b-a3b-... Compressed-tensors Conversational Endpoints compatible Quantized Qwen3 moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/cpatonn/Qwen3-30B-A3B-Thinking-2507-AWQ

Qwen3 30B A3B Thinking 2507 AWQ Parameters and Internals

LLM Name	Qwen3 30B A3B Thinking 2507 AWQ
Repository 🤗	https://huggingface.co/cpatonn/Qwen3-30B-A3B-Thinking-2507-AWQ
Base Model(s)	Qwen3 30B A3B Thinking 2507 Qwen/Qwen3-30B-A3B-Thinking-2507
Model Size	30b
Required VRAM	16.7 GB
Updated	2025-08-08
Maintainer	cpatonn
Model Type	qwen3_moe
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 5.0 GB: 3-of-4 1.7 GB: 4-of-4
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	Qwen3MoeForCausalLM
License	apache-2.0
Context Length	262144
Model Max Length	262144
Transformers Version	4.52.4
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen3 30B A3B Thinking 2507 AWQ 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 A3B Thinking 2507 AWQ

Best Alternatives	Context / RAM	Downloads	Likes
...30B A3B Instruct 2507 AWQ 4bit	256K / 18.1 GB	744207	32
...oder 30B A3B Instruct AWQ 4bit	256K / 18.1 GB	582899	56
...30B A3B Thinking 2507 AWQ 4bit	256K / 18.1 GB	306994	15
...oder 30B A3B Instruct AWQ 4bit	256K / 18.1 GB	165162	23
...30B A3B Thinking 2507 AWQ 4bit	256K / 16.7 GB	67401	8
...30B A3B Instruct 2507 AWQ 4bit	256K / 18.1 GB	33039	14
...wen3 30B A3B Instruct 2507 AWQ	256K / 16.7 GB	3531	6
...en3 Coder 30B A3B Instruct AWQ	256K / 16.7 GB	2464	3
Qwen3 30B A3B AWQ	40K / 16.8 GB	41826	13
Qwen3 30B A3B AWQ	40K / 16.8 GB	4483	12

Note: green Score (e.g. "73.2") means that the model is better than cpatonn/Qwen3-30B-A3B-Thinking-2507-AWQ.