Run AI workloads on any cloud, store on Hugging Face: zero-egress storage with SkyPilot

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. 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 hf:// URL and the HF_TOKEN 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. store: hf mounts a Hugging Face Bucket (read-write) or any model / dataset / Space repo (read-only) into a SkyPilot task with one hf:// URL and your existing HF_TOKEN, via MOUNT or COPY.
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.
No egress to read your data. 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.
Xet-backed dedup. Buckets are built on Xet, so incremental checkpoints and model variants only store and transfer the chunks that changed.
Built together. Hugging Face and SkyPilot shipped this jointly, and the Hugging Face team upstreamed the hf-mount FUSE fixes that make it work in unprivileged containers.