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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallHugging Face acquired Seattle-based XetHub in a deal announced on August 8, 2024, to bring its large-file storage and versioning technology into the Hugging Face Hub. The acquisition was chiefly about storing, updating and distributing large AI models and datasets more efficiently—not about adding a new GPU-compute service or a competing model marketplace.
What Hugging Face bought—and what it did not
XetHub was a startup focused on infrastructure for large, changing files: storage, versioning and collaboration for AI development. Hugging Face said it would integrate XetHub’s technology into the Hub. The company’s announcement named co-founders Yucheng Low, Ajit Banerjee and Rajat Arya, described the team as 12 people, and did not disclose the purchase price. Hugging Face’s acquisition announcement also described the goal as making software-engineering practices work better for AI development.
The founders’ Apple experience was relevant to that infrastructure focus. The announcement said Low had worked on AI data management at Apple at a scale exceeding 100 PB, supporting dozens of internal teams and hundreds of features annually. Those are figures reported in the announcement, not independently audited metrics.
At the time, VentureBeat reported that the standalone XetHub platform would cease to exist as a separate product as its capabilities moved into Hugging Face. That does not establish that every historical XetHub account or workflow migrated automatically. VentureBeat’s acquisition coverage provides that product-transition detail.
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The acquisition did not, by itself, add training or inference compute. It concerned artifact storage, version control and distribution: keeping model weights and datasets in repositories and delivering them to users. Those are distinct from running a model or training it on GPUs.
Why large AI files strain familiar Git workflows
Git is designed around source-code history, where changes are often small text edits. Model checkpoints and datasets can instead be gigabytes or larger, and are binary files. Git LFS addresses that mismatch by keeping pointer files in Git while storing the large content remotely. In the Hugging Face comparison, the key limitation is that Git LFS deduplicates at the file level: a revised binary may be treated as a new large object even if much of its content is unchanged.
For a team revising a checkpoint or adding data to a dataset, repeatedly transferring and retaining largely overlapping versions can consume bandwidth, time and storage. Xet is designed to identify reusable portions inside large files, which can make iterative work more efficient without abandoning a Git-like repository workflow.
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How Xet differs from Git LFS
| Approach | How large files are handled | What happens when a file changes |
|---|---|---|
| Git LFS | Git stores a lightweight pointer; the binary content is stored remotely. | Deduplication is generally file-level, so a revised large file can be handled as a new object. |
| Xet | Large content is represented through pointer conventions and stored using chunk-based, content-addressed mechanisms. | Unchanged chunks can be reused; changed portions can be transferred or stored separately. |
Hugging Face describes Xet as offering chunk-level or byte-level deduplication. In practical terms, imagine a 400-GB checkpoint where one section changes: Xet may be able to reuse unchanged chunks instead of retransferring the whole revised file. That is an illustration of the mechanism, not a measured benchmark or guaranteed transfer time. The benefit depends on how much content revisions share, the file structure, the client, caching and the network.
The Hub has adopted Xet as its modern storage system for large AI and ML files, while keeping Git LFS support for backward compatibility. Files continue to work with familiar repository conventions, and a compatibility bridge allows older clients to access Xet-backed content. See the Xet overview, Xet documentation and Git LFS compatibility guidance.
What changes for Hub users
For most users, the repository and web experience remains familiar. Existing repositories do not necessarily need a manual conversion, and older non-Xet-aware clients can use the LFS bridge. Newer client libraries and Git-Xet can take advantage of Xet-aware transfers. Ordinary workflows through libraries such as transformers and datasets generally rely on huggingface_hub, so the installed Hub client matters.
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Python downloads and uploads
Hugging Face’s documented guidance says huggingface_hub version 0.32.0 and later installs hf_xet. For versions 0.30.0 through below 0.32.0, it says to install the Xet package explicitly. Package behavior can change, so consult the current Xet usage instructions for the version you install.
pip install -U huggingface_hubFor the documented 0.30.0-to-below-0.32.0 version range, install the additional package with:
pip install -U hf-xetGit-based repository workflows
For direct Git work, install and configure Git-Xet if you want Xet-aware handling. The documented installation options include:
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brew install git-xet
git xet installOn macOS or Linux, the documentation also provides this installation script:
curl --proto '=https' -tlsv1.2 -sSf https://raw.githubusercontent.com/huggingface/xet-core/refs/heads/main/git_xet/install.sh | shOn Windows, its WinGet command is:
winget install git-xetCheck that the command is available, then use normal Git commands to commit and push:
git xet --version
git add .
git commit -m "Uploading new models"
git pushIf a file extension is not tracked as expected, the repository setup documentation describes how to add a pattern, for example git xet track "*.your_extension". Use a pattern that matches the files in your repository, and check that Git LFS prerequisites and the repository’s .gitattributes are configured appropriately. Instructions are available in Hugging Face’s repository setup guide.
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Where Xet helps—and where it does not
Xet is most useful when large artifacts change repeatedly and successive versions share content: for example, evolving checkpoints or datasets that are extended over time. In those cases, chunk reuse can reduce redundant storage and transfer. A one-time upload of unrelated files, or revisions with little shared content, may benefit less. Compressed or encrypted files can also offer less reusable internal structure. These are workload-dependent engineering implications, not guaranteed outcomes.
- It does not eliminate full downloads. A user who needs an entire multi-gigabyte model still has to receive that artifact.
- It does not supply compute. Storage efficiency is not GPU capacity, model training, or inference hosting.
- It does not remove operational requirements. Users still need adequate local disk space, network access, credentials and repository permissions.
- It does not resolve data governance. Licensing, provenance, privacy and model security remain separate concerns.
- It is not guaranteed to make every transfer faster. Network path, concurrency, client version, caching and the amount of reusable content all matter.
If a transfer remains slow, check the client version and Xet setup, then consider network or regional connectivity, server-side concurrency and whether the operation is downloading a complete artifact rather than an incremental update. Hugging Face’s download guidance and Hub environment-variable reference document relevant client behavior, including the local HF_HUB_DISABLE_XET setting.
What the migration says about the Hub
The acquisition’s significance is clearer now that Xet is an operating part of Hub storage rather than only a planned integration. A Hugging Face migration report said that within six months, 500,000 repositories containing 20 PB had joined the move to Xet. That is a dated migration milestone, not a current total for all Hub repositories or stored data. The report is available at Hugging Face’s Xet migration report.
For publishers, the aim is to make large, evolving artifacts more practical to version and distribute through the Hub’s existing ecosystem. The acquisition announcement also described ambitions around reproducibility, collaboration and visualizing how datasets and models change over time. It should not be read as a promise of unlimited storage or compute; current plan terms, quotas and products are separate and can be checked on the Hugging Face pricing page.
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Xet is part of Hugging Face’s model-and-dataset Hub; it is not a universal substitute for object storage, data-versioning tools or managed ML platforms. The right comparison depends on whether a team values a public ML discovery and sharing ecosystem, direct control of cloud storage, Git-like data workflows, or an end-to-end managed platform.
| Option | Best understood as | Trade-off versus the Hub and Xet |
|---|---|---|
| Git LFS | Git-compatible large-file storage | Familiar and still supported for compatibility on the Hub; the Hub comparison describes file-level rather than chunk-level deduplication. |
| Amazon S3, Google Cloud Storage, or Azure Blob Storage | General-purpose cloud object storage | More direct control over buckets, access policies and lifecycle management, but teams may need to build their own registry, metadata, versioning and sharing layer. |
| DVC or lakeFS | Data and model versioning or Git-like data-lake workflows | Can support workflows outside the Hub ecosystem; they do not provide the same integrated public model-discovery community. |
| AWS SageMaker, Google Vertex AI, Azure Machine Learning, or Databricks Machine Learning | Broader managed ML platforms | More focused on managed development, training, deployment or governance than on serving as a public open-model repository. |
Pricing should be compared only using current terms: storage, bandwidth, inference and accelerator compute are separate costs, and Xet’s deduplication does not make them free. The Hub’s live pricing page is the relevant source for its current plans and usage terms.
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