What are the hardware requirements for Qwen3.5 2B Fas 16384?

Qwen3.5 2B Fas 16384 requires approximately 3.5 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.5 2B Fas 16384 and how large is it?

Qwen3.5 2B Fas 16384 is developed by alphaedge-ai, a model with 2b 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 Qwen3.5 2B Fas 16384?

Qwen3.5 2B Fas 16384 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.5 2B Fas 16384 by alphaedge-ai — VRAM 3.5GB

Name: Qwen3.5 2B Fas 16384
Author: alphaedge-ai

Qwen3.5 2B Fas 16384 is an open-source language model by alphaedge-ai. Features: 2b LLM, VRAM: 3.5GB, License: apache-2.0.

Conversational Dataset:lbourdois/fineweb-2-tr... Endpoints compatible Fas Image-text-to-text Qwen3 5 Region:eu Safetensors Trimmed

Model Card on HF 🤗: https://huggingface.co/alphaedge-ai/Qwen3.5-2B-fas-16384

Qwen3.5 2B Fas 16384 Parameters and Internals

LLM Name	Qwen3.5 2B Fas 16384
Repository 🤗	https://huggingface.co/alphaedge-ai/Qwen3.5-2B-fas-16384
Base Model(s)	Qwen3.5-2B /Qwen3.5-2B
Model Size	2b
Required VRAM	3.5 GB
Updated	2026-09-14
Maintainer	alphaedge-ai
Model Type	qwen3_5
Model Files	3.5 GB
Model Architecture	Qwen3_5ForConditionalGeneration
License	apache-2.0
Model Max Length	262144
Transformers Version	4.57.0.dev0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Qwen3.5 2B Fas 16384 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

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Note: green Score (e.g. "73.2") means that the model is better than alphaedge-ai/Qwen3.5-2B-fas-16384.