What are the hardware requirements for Qwen3 8B DFlash B16?

Qwen3 8B DFlash B16 requires approximately 2.1 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 Qwen3 8B DFlash B16 and how large is it?

Qwen3 8B DFlash B16 is developed by z-lab, 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 Qwen3 8B DFlash B16?

Qwen3 8B DFlash B16 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3 8B DFlash B16 by z-lab — VRAM 2.1GB, 40K context

Name: Qwen3 8B DFlash B16
Author: z-lab

Qwen3 8B DFlash B16 is an open-source language model by z-lab. Features: 8b LLM, VRAM: 2.1GB, Context: 40K, License: mit, LLM Explorer Score: 0.3.

Arxiv:2602.06036 Custom code Dflash Diffusion Diffusion-language-model Efficiency Endpoints compatible Feature-extraction Flash-decoding Qwen Qwen3 Region:us Safetensors Speculative-decoding

Model Card on HF 🤗: https://huggingface.co/z-lab/Qwen3-8B-DFlash-b16

Qwen3 8B DFlash B16 Parameters and Internals

LLM Name	Qwen3 8B DFlash B16
Repository 🤗	https://huggingface.co/z-lab/Qwen3-8B-DFlash-b16
Model Size	8b
Required VRAM	2.1 GB
Updated	2026-08-04
Maintainer	z-lab
Model Type	qwen3
Model Files	2.1 GB
Model Architecture	DFlashDraftModel
License	mit
Context Length	40960
Model Max Length	40960
Transformers Version	4.57.3
Vocabulary Size	151936

Rank the Qwen3 8B DFlash B16 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