Twitter/X

Hugging Face is positioning its Hub and Storage Buckets as the go-to place for…

2026-06-23 · 17:42 UTC ·@ClementDelangue ·1 min read

Brief

Hugging Face is positioning its Hub and Storage Buckets as the preferred back-end for massive, append-only datasets in robotics and video AI. Clement Delangue cites public robotics data rising from 1,000 (early 2025) to 60,000 (2026-06-23), robot recording at 140 MB/s, and Hub streaming with pre-warmed cache delivering ~1,326 MB/s (LeRobot example).

Why it matters

Hugging Face is positioning its Hub and Storage Buckets as the go-to place for both public and private large-scale datasets, especially for robotics and video AI.

Key details

  • Public robotics datasets grew from 1,000 in early 2025 to 60,000 by 2026-06-23, with private robotics datasets reported at about 2× that number.
  • A single robot can record at 140 MB/s continuously; mismanaging storage/streaming can leave GPUs idle at 0 MB/s, while streaming straight from the Hub with a pre-warmed cache (LeRobot + Hugging Face Storage Buckets) can feed GPUs at ~1,326 MB/s.
Source evidence

HF is quietly becoming the best place to store data, public AND private, especially for brutal domains like robotics and video AI where the files are massive, append-only, and never stop growing.

Example? Public robotics datasets exploded from 1,000 in early 2025 to 60,000 today, and there's twice as many private ones.

Why? A single robot records at 140 MB/s, all day, forever. That data has to be stored, streamed to GPUs, and shipped back to hardware on repeat. Get it wrong and your GPUs sit idle at 0 MB/s waiting for a dataset to land. Get it right (stream straight from the Hub, pre-warmed cache) and those same GPUs scream along at ~1,326 MB/s, fully fed. 🚀

Here's how LeRobot + Hugging Face Storage Buckets pull it off: huggingface.co/spaces/imstev…