What are the hardware requirements for D3 Mini?

D3 Mini requires approximately 9.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed D3 Mini and how large is it?

D3 Mini is developed by vllm-sr, a model with 4b 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 D3 Mini?

D3 Mini is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

D3 Mini by vllm-sr — VRAM 9.1GB

Name: D3 Mini
Author: vllm-sr

D3 Mini is an open-source language model by vllm-sr. Features: 4b LLM, VRAM: 9.1GB, License: apache-2.0, LLM Explorer Score: 0.41.

Base model:finetune:qwen/qwen3... Base model:qwen/qwen3.5-4b Classification Custom code Decision-model Endpoints compatible Feature-extraction Multimodal Qwen3 5 Region:us Safetensors Sharded System-one Tensorflow Video Vision Zero-shot-classification

Model Card on HF 🤗: https://huggingface.co/vllm-sr/d3-mini

D3 Mini Parameters and Internals

LLM Name	D3 Mini
Repository 🤗	https://huggingface.co/vllm-sr/d3-mini
Base Model(s)	Qwen/Qwen3.5-4B Qwen/Qwen3.5-4B
Model Size	4b
Required VRAM	9.1 GB
Updated	2026-10-15
Maintainer	vllm-sr
Model Type	qwen3_5
Model Files	5.0 GB: 1-of-2 4.1 GB: 2-of-2 0.0 GB
Model Architecture	Qwen3_5Model
License	apache-2.0
Model Max Length	262144
Transformers Version	5.17.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the D3 Mini 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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