What are the hardware requirements for Chandra 4bit?

Chandra 4bit requires approximately 5.7 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Chandra 4bit and how large is it?

Chandra 4bit is developed by mlx-community. 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 Chandra 4bit?

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

Chandra 4bit by mlx-community — VRAM 5.7GB

Name: Chandra 4bit
Author: mlx-community

Chandra 4bit is an open-source language model by mlx-community. Features: LLM, VRAM: 5.7GB, License: openrail, Quantized, LLM Explorer Score: 0.21.

4-bit 4bit Conversational Endpoints compatible Image-text-to-text Mlx Ocr Quantized Qwen3 vl Region:us Safetensors Sharded Tensorflow Vlm

Model Card on HF 🤗: https://huggingface.co/mlx-community/chandra-4bit

Chandra 4bit Parameters and Internals

LLM Name	Chandra 4bit
Repository 🤗	https://huggingface.co/mlx-community/chandra-4bit
Required VRAM	5.7 GB
Updated	2026-08-08
Maintainer	mlx-community
Model Type	qwen3_vl
Model Files	5.3 GB: 1-of-2 0.4 GB: 2-of-2
Quantization Type	4bit
Model Architecture	Qwen3VLForConditionalGeneration
License	openrail
Model Max Length	262144
Transformers Version	4.57.1
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Chandra 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

Best Alternatives to Chandra 4bit

Best Alternatives	Context / RAM	Downloads	Likes
Chandra 8bit	0K / 9.8 GB	52	2