What are the hardware requirements for Ling 3.0 Tiny MLX 4bit?

Ling 3.0 Tiny MLX 4bit requires approximately 4.5 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 Ling 3.0 Tiny MLX 4bit and how large is it?

Ling 3.0 Tiny MLX 4bit is developed by rapid-mlx, a model with 7.9b 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 Ling 3.0 Tiny MLX 4bit?

Ling 3.0 Tiny MLX 4bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Ling 3.0 Tiny MLX 4bit by rapid-mlx — VRAM 4.5GB, 128K context

Name: Ling 3.0 Tiny MLX 4bit
Author: rapid-mlx

Ling 3.0 Tiny MLX 4bit is an open-source language model by rapid-mlx. Features: 7.9b LLM, VRAM: 4.5GB, Context: 128K, License: mit, Quantized, LLM Explorer Score: 0.37.

4-bit 4bit Apple-silicon Bailing hybrid Base model:inclusionai/ling-3.... Base model:quantized:inclusion... Conversational Custom code Ling Mlx Moe Quantized Rapid-mlx Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/rapid-mlx/Ling-3.0-tiny-MLX-4bit

Ling 3.0 Tiny MLX 4bit Parameters and Internals

LLM Name	Ling 3.0 Tiny MLX 4bit
Repository 🤗	https://huggingface.co/rapid-mlx/Ling-3.0-tiny-MLX-4bit
Base Model(s)	inclusionAI/Ling-3.0-tiny inclusionAI/Ling-3.0-tiny
Model Size	7.9b
Required VRAM	4.5 GB
Updated	2026-10-25
Maintainer	rapid-mlx
Model Type	bailing_hybrid
Model Files	4.5 GB
Quantization Type	4bit
Model Architecture	BailingMoeV3ForCausalLM
License	mit
Context Length	131072
Model Max Length	131072
Transformers Version	4.45.0
Tokenizer Class	TokenizersBackend
Padding Token	<\|endoftext\|>
Vocabulary Size	157184
Torch Data Type	bfloat16

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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
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Multi-Language Support and Translation

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