Run AI workloads on any cloud, store on Hugging Face: zero-egress storage with SkyPilot
We're on a journey to advance and democratize artificial intelligence through open source and open science. Back to Articles a]:hidden"> Run AI workloads on any cloud, store on Hugging Face: zero-egress storage with SkyPilot Published July 7, 2026 Update on GitHub Upvote - Nikhil Jha njha Follow guest For most teams, models and datasets live in a bucket in one region of one cloud. The GPUs you can get, whether for development, training, or serving, increasingly sit on a different cloud than your data.
Key Takeaways
- The moment those two come apart, you pay a cross-cloud transfer tax just to read your own data onto your own GPUs.
Together with Hugging Face, we've joined the two halves: your models and datasets stay on the Hub, and SkyPilot runs the compute (dev, training, or serving) on whatever cluster has the GPUs.
- mounts a Hugging Face Bucket (read-write) or any model / dataset / Space repo (read-only) into a SkyPilot task with one URL and your existing , via or .
- Hugging Face Storage is now a first-class SkyPilot backend SkyPilot tasks already read and write cloud object stores (S3, GCS, Azure, R2, and many more) by mounting them at a local path.
- For the store, and behave the same, so either mode keeps the cache.
Because reads are lazy, a process can start working through a large file before the whole file has downloaded, instead of blocking on a full copy first.
- Set in your environment and hand it to a run with ; SkyPilot uses it for the mount on whatever cloud the job lands.
Stats & Key Facts
- #Back to Articles a]:hidden"> Run AI workloads on any cloud, store on Hugging Face: zero-egress storage with SkyPilot Published July 7, 2026 Update on GitHub Upvote - Nikhil Jha njha Follow guest For most teams, models and datasets live in a bucket in one region of one cloud.
The moment those two come apart, you pay a cross-cloud transfer tax just to read your own data onto your own GPUs. Together with Hugging Face, we've joined the two halves: your models and datasets stay on the Hub, and SkyPilot runs the compute (dev, training, or serving) on whatever cluster has the GPUs. Mount a Hugging Face Bucket or any Hub repo into a SkyPilot job with one URL and the you already have, then launch it wherever capacity is.
Hugging Face charges no egress, so reading your data onto those GPUs costs nothing, on any cloud. Here's what's new: Your Hub data in any job. mounts a Hugging Face Bucket (read-write) or any model / dataset / Space repo (read-only) into a SkyPilot task with one URL and your existing , via or .
Run it on any GPU, on any cloud. SkyPilot finds that job compute across 20+ clouds, Kubernetes, Slurm, and on-prem, so the same run uses whichever of your reserved or on-demand GPUs is available, on any vendor. Hugging Face Storage charges no egress or CDN fees , so wherever SkyPilot lands the job, it reads your models and datasets straight from the same bucket, with no per-cloud copies and no egress bill to pull them in.
For more details please read the original article at Hugging Face.
Continue Learning
Comments
Sign in to join the conversation