What are the hardware requirements for SearchQwen3 8B?

SearchQwen3 8B requires approximately 16.4 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 SearchQwen3 8B and how large is it?

SearchQwen3 8B is developed by alibaba-pai, a model with 8b 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 SearchQwen3 8B?

SearchQwen3 8B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

SearchQwen3 8B by alibaba-pai — VRAM 16.4GB, 40K context

Name: SearchQwen3 8B
Author: alibaba-pai

SearchQwen3 8B is an open-source language model by alibaba-pai. Features: 8b LLM, VRAM: 16.4GB, Context: 40K, License: apache-2.0, LLM Explorer Score: 0.35.

Base model:finetune:qwen/qwen3... Base model:qwen/qwen3-8b Conversational Deep-search En Endpoints compatible Function-calling Qwen Qwen3 Region:us Safetensors Search-agent Sharded Tensorflow Tool-use

Model Card on HF 🤗: https://huggingface.co/alibaba-pai/SearchQwen3-8B

SearchQwen3 8B Parameters and Internals

LLM Name	SearchQwen3 8B
Repository 🤗	https://huggingface.co/alibaba-pai/SearchQwen3-8B
Base Model(s)	Qwen3 8B Qwen/Qwen3-8B
Model Size	8b
Required VRAM	16.4 GB
Updated	2026-08-26
Maintainer	alibaba-pai
Model Type	qwen3
Model Files	4.1 GB: 1-of-4 5.0 GB: 2-of-4 5.0 GB: 3-of-4 2.3 GB: 4-of-4
Supported Languages	en
Model Architecture	Qwen3ForCausalLM
License	apache-2.0
Context Length	40960
Model Max Length	40960
Transformers Version	4.56.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Errors	replace

Rank the SearchQwen3 8B capabilities

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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

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