What are the hardware requirements for FastVLM 0.5B Bf16?

FastVLM 0.5B Bf16 requires approximately 1.2 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed FastVLM 0.5B Bf16 and how large is it?

FastVLM 0.5B Bf16 is developed by mlx-community, a model with 0.5b 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 FastVLM 0.5B Bf16?

FastVLM 0.5B Bf16 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

FastVLM 0.5B Bf16 by mlx-community — VRAM 1.2GB, 32K context

Name: FastVLM 0.5B Bf16
Author: mlx-community

FastVLM 0.5B Bf16 is an open-source language model by mlx-community. Features: 0.5b LLM, VRAM: 1.2GB, Context: 32K, License: apple-amlr, LLM Explorer Score: 0.24.

Conversational Custom code Image-text-to-text Llava qwen2 Mlx Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/mlx-community/FastVLM-0.5B-bf16

FastVLM 0.5B Bf16 Parameters and Internals

LLM Name	FastVLM 0.5B Bf16
Repository 🤗	https://huggingface.co/mlx-community/FastVLM-0.5B-bf16
Model Size	0.5b
Required VRAM	1.2 GB
Updated	2026-08-08
Maintainer	mlx-community
Model Type	llava_qwen2
Model Files	1.2 GB
Model Architecture	LlavaQwen2ForCausalLM
License	apple-amlr
Context Length	32768
Model Max Length	32768
Transformers Version	4.56.1
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Errors	replace

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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

Best Alternatives to FastVLM 0.5B Bf16

Best Alternatives	Context / RAM	Downloads	Likes
Llavaqwen2.5 0.5B Finetune	32K / 1.9 GB	8	0
FastVLM 0.5B Stage3	32K / 1.5 GB	15	2