What are the hardware requirements for Qwen3.8 4B Distilled GGUF?

Qwen3.8 4B Distilled GGUF requires approximately 2.5 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 Qwen3.8 4B Distilled GGUF and how large is it?

Qwen3.8 4B Distilled GGUF is developed by Ma7ee7, 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 Qwen3.8 4B Distilled GGUF?

Qwen3.8 4B Distilled GGUF is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.8 4B Distilled GGUF by Ma7ee7 — VRAM 2.5GB, 256K context

Name: Qwen3.8 4B Distilled GGUF
Author: Ma7ee7

Qwen3.8 4B Distilled GGUF is an open-source language model by Ma7ee7. Features: 4b LLM, VRAM: 2.5GB, Context: 256K, License: apache-2.0, Quantized, LLM Explorer Score: 0.39.

Base model:ma7ee7/qwen3.8 4b d... Base model:quantized:ma7ee7/qw... Chain-of-thought Conversational Dataset:r0b0tlab/qwen3.8-max-d... Distillation En Endpoints compatible Gguf Knowledge-distillation Llama.cpp Quantized Qwen Qwen3 Qwen3.8 Reasoning Region:us Sequence-level-distillation Thinking

Model Card on HF 🤗: https://huggingface.co/Ma7ee7/Qwen3.8_4B_Distilled_GGUF

Qwen3.8 4B Distilled GGUF Parameters and Internals

LLM Name	Qwen3.8 4B Distilled GGUF
Repository 🤗	https://huggingface.co/Ma7ee7/Qwen3.8_4B_Distilled_GGUF
Base Model(s)	Ma7ee7/Qwen3.8_4B_Distilled Ma7ee7/Qwen3.8_4B_Distilled
Model Size	4b
Required VRAM	2.5 GB
Updated	2026-08-09
Maintainer	Ma7ee7
Model Type	qwen3
Model Files	2.5 GB 2.9 GB 4.3 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	Qwen3ForCausalLM
License	apache-2.0
Context Length	262144
Model Max Length	262144
Vocabulary Size	151936
Torch Data Type	bfloat16

Rank the Qwen3.8 4B Distilled GGUF 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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