What are the hardware requirements for Qwen3 30B A3B Instruct 2507 FP8?

Qwen3 30B A3B Instruct 2507 FP8 requires approximately 31.2 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 Instruct 2507 FP8 and how large is it?

Qwen3 30B A3B Instruct 2507 FP8 is developed by unsloth, 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 Instruct 2507 FP8?

Qwen3 30B A3B Instruct 2507 FP8 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3 30B A3B Instruct 2507 FP8 by unsloth — VRAM 31.2GB, 256K context

Name: Qwen3 30B A3B Instruct 2507 FP8
Author: unsloth

Qwen3 30B A3B Instruct 2507 FP8 is an open-source language model by unsloth. Features: 30b LLM, VRAM: 31.2GB, Context: 256K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.2.

Arxiv:2505.09388 Base model:quantized:qwen/qwen... Base model:qwen/qwen3-30b-a3b-... Conversational Endpoints compatible Fp8 Instruct Qwen3 moe Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/unsloth/Qwen3-30B-A3B-Instruct-2507-FP8

Qwen3 30B A3B Instruct 2507 FP8 Parameters and Internals

LLM Name	Qwen3 30B A3B Instruct 2507 FP8
Repository 🤗	https://huggingface.co/unsloth/Qwen3-30B-A3B-Instruct-2507-FP8
Base Model(s)	...wen3 30B A3B Instruct 2507 FP8 Qwen/Qwen3-30B-A3B-Instruct-2507-FP8
Model Size	30b
Required VRAM	31.2 GB
Updated	2026-05-22
Maintainer	unsloth
Model Type	qwen3_moe
Instruction-Based	Yes
Model Files	10.0 GB: 1-of-4 10.0 GB: 2-of-4 10.0 GB: 3-of-4 1.2 GB: 4-of-4
Model Architecture	Qwen3MoeForCausalLM
License	apache-2.0
Context Length	262144
Model Max Length	262144
Transformers Version	4.54.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|vision_pad\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen3 30B A3B Instruct 2507 FP8 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 Instruct 2507 FP8

Best Alternatives	Context / RAM	Downloads	Likes
...LL MASTER CODER M 1million Ctx	1024K / 84.8 GB	173	11
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
Lynx Instruct 30B	256K / 61.1 GB	223244	4

Note: green Score (e.g. "73.2") means that the model is better than unsloth/Qwen3-30B-A3B-Instruct-2507-FP8.