What are the hardware requirements for VibeThinker 3B OptiQ 4bit?

VibeThinker 3B OptiQ 4bit requires approximately 2.3 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed VibeThinker 3B OptiQ 4bit and how large is it?

VibeThinker 3B OptiQ 4bit is developed by mlx-community, a model with 3b 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 VibeThinker 3B OptiQ 4bit?

VibeThinker 3B OptiQ 4bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

VibeThinker 3B OptiQ 4bit by mlx-community — VRAM 2.3GB, 128K context

Name: VibeThinker 3B OptiQ 4bit
Author: mlx-community

VibeThinker 3B OptiQ 4bit is an open-source language model by mlx-community. Features: 3b LLM, VRAM: 2.3GB, Context: 128K, License: mit, Quantized, LLM Explorer Score: 0.26.

4-bit 4bit 8bit Apple-silicon Base model:mlx-community/vibet... Base model:quantized:mlx-commu... Conversational Mixed-precision Mlx Optiq Quantized Qwen2 Reasoning Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/mlx-community/VibeThinker-3B-OptiQ-4bit

VibeThinker 3B OptiQ 4bit Parameters and Internals

LLM Name	VibeThinker 3B OptiQ 4bit
Repository 🤗	https://huggingface.co/mlx-community/VibeThinker-3B-OptiQ-4bit
Base Model(s)	VibeThinker 3B mlx-community/VibeThinker-3B
Model Size	3b
Required VRAM	2.3 GB
Updated	2026-07-06
Maintainer	mlx-community
Model Type	qwen2
Model Files	2.3 GB
Quantization Type	4bit
Model Architecture	Qwen2ForCausalLM
License	mit
Context Length	131072
Model Max Length	131072
Transformers Version	4.51.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the VibeThinker 3B OptiQ 4bit 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 mlx-community/VibeThinker-3B-OptiQ-4bit.