# FastVLM vs SmolVLM2

FastVLM (Apple, 0.5B–7B) and SmolVLM2 (Hugging Face, 256M–2.2B) compared: sizes, image and video support, browser and MLX runtimes, vision encoders, licenses and when to pick each.

Canonical URL: https://aiidelist.com/blog/fastvlm-vs-smolvlm2

Language: en

Published: 2026-09-24

Updated: 2026-09-24

FASTVLM VS SMOLVLM2

Both families target devices, not data centres. FastVLM optimises time-to-first-token on high-resolution images with its FastViTHD encoder; SmolVLM2 pushes size down to 256M and adds video. This page compares what is documented, not what a single benchmark table implies.

[All comparisons](/fastvlm#comparisons)

| Feature | FastVLM | SmolVLM2 |
| --- | --- | --- |
| Developer | Apple researchers (CVPR 2025) | Hugging Face (HuggingFaceTB) |
| Sizes | 0.5B · 1.5B · 7B | 256M · 500M · 2.2B |
| Vision encoder | FastViTHD hybrid encoder; fewer visual tokens at high resolution | SigLIP-family encoder with pixel-shuffle token compression |
| Image input | Yes | Yes |
| Video input | Not in the official models (apps process frames one at a time) | Yes — Video-Instruct variants at 256M, 500M and 2.2B |
| Multi-image prompts | Single image per prompt in official examples | Yes |
| Browser (WebGPU) | 0.5B via onnx-community/FastVLM-0.5B-ONNX (this site’s playground) | SmolVLM 256M/500M have Transformers.js demos; check the SmolVLM2 card for ONNX |
| MLX / Apple Silicon | Official MLX app for iOS and macOS; mlx-vlm | MLX from day zero (Python and Swift) |
| License | Apple ML Research license (read the terms) | Apache 2.0 |
| Headline claim | Up to 85× faster TTFT than LLaVA-OneVision-0.5B at 1152 px (Apple) | Smallest video-capable VLMs; 500M close to 2.2B on video (Hugging Face) |

Checked 2026-09-17

## Decision summary

- Pick FastVLM for high-resolution screenshots, receipts and documents where first-token latency matters, especially on Apple hardware.
- Pick SmolVLM2 when you need video, multi-image prompts, a sub-500M model or an Apache 2.0 license.
- Both run in the browser at their smallest sizes; FastVLM-0.5B is the one you can test on this site right now.

## Choose FastVLM when

- Inputs are dense documents or UI screenshots at 1024 px and above
- You deploy on iPhone or Mac with MLX or Core ML
- You want the reference 0.5B browser demo Apple links from its research page

## Choose SmolVLM2 when

- You need short-video understanding or several images per prompt
- Memory is extremely tight (256M–500M)
- License simplicity matters for a commercial product

## How to read this table

Published scores for the two families come from different papers, prompts and evaluation code. Do not rank them from a merged table; run both on your own images with the same resolution and device.

## Questions

**Is SmolVLM2 more accurate than FastVLM?**

There is no apples-to-apples published comparison. FastVLM-0.5B reports DocVQA 82.5 and TextVQA 64.5 in Apple’s table; SmolVLM2’s cards report their own suites. Test on your data.

**Which is faster in the browser?**

It depends on the GPU, quantization and image size. Use the device benchmark on this site to measure FastVLM-0.5B and compare against a SmolVLM demo on the same machine.

## Sources

- [FastVLM paper](https://arxiv.org/abs/2412.13303)
- [SmolVLM2 announcement](https://huggingface.co/blog/smolvlm2)
- [SmolVLM 256M/500M browser demo post](https://huggingface.co/blog/smolervlm)

## Continue

- [On-device VLM hub](/blog/on-device-vlm-comparison)
- [Benchmark your device](/blog/benchmark-fastvlm-device)
- [FastVLM vs LFM2.5-VL](/blog/fastvlm-vs-lfm2-5-vl)