Git LFS vs Xet: Is Xet the Better Storage for AI?

⚙️ Hugging Face cut 50% of bandwidth by switching from Git LFS to Xet.
🚀 Xet cloning is 10–20x faster for repos with over 5GB of binaries.
🔁 Git LFS treats slightly edited files as full duplicates—Xet stores only deltas.
🧠 Xet allows for real-time sync and diffing even with complex AI models.
💡 Automation platforms like Make.com get quick pipeline updates with Xet.

Today's AI workflows need large binary files, frequent updates, and fast teamwork. But the tools we use for managing this data often fall behind. Git is common for source code, but it has always had trouble with large files. Git Large File Storage (Git LFS) helped with that, but it now shows its limits at AI scale. As datasets grow and automation teams work faster, these limits become major problems. Xet is a solution built for Git. It performs well and changes how large AI files and repositories are stored, synced, and shared.

Git LFS: Progress, Then Friction at Scale

Git LFS, or Git Large File Storage, was made to fix a main problem with regular Git systems. They could not handle large binary files well. Git LFS does not store big files like datasets or model checkpoints right inside the .git directory. Instead, it replaces them with small pointers. The actual files stay on a distant LFS server and are downloaded when needed.

This approach worked for a while.

The Benefits Were Clear (at First)

Developers could track big files in Git without making every commit too large.
People working together did not need to share outside links or use S3 manually.
The Git workflow stayed the same: commits, branches, and pull requests all worked as expected.

But companies building and using AI at a large scale started to see problems.

The Problems Begin

When AI datasets grew to dozens or hundreds of gigabytes, and model binaries grew past several gigabytes, Git LFS began to struggle. Here are some of the main problems:

🚫 Slow Clone and Pull Times

Every time someone copied or updated a repo, Git LFS downloaded every file needed. This happened even if the files were not needed right away. For machine learning teams, this often meant getting huge datasets just to change a config file.

📦 No Smart Deduplication

LFS did not support block-level deduplication. A small change to a large file created a new version of that file. This meant redownloading and reuploading gigabytes, even if only a few bytes had changed.

⏳ Collaboration Got Harder

Everyone working on a project needed the full LFS context to work right. This made it harder to bring new developers or team members on board. CI/CD pipelines became slow. LFS setup problems often caused delays or build failures.

👀 History Blindness

Git's usual diff and merge tools cannot see inside binary files. You could not check or follow how model file versions changed, only see the versions themselves.

For teams managing many AI models—across multiple clients, languages, or setups—these problems often made Git LFS more trouble than it was worth.

Meet Xet: Git-Native Storage Built for AI and Automation

Xet was built from scratch for AI data sizes. It changes how Git works to handle large files easily. It does this by putting deduplicated, chunked object storage right into the Git interface.

What does that mean? You can store hundreds of gigabytes and keep track of their versions. You will not lose Git speed or ease of use.

Xet vs Git LFS: Head-to-Head Comparison

Feature	Git LFS	Xet
File Handling	Pointer-based	Chunk-based object store
Deduplication	Partial	Full block-level
Diff Support	Limited (no insight into content)	Full diff, including structured formats like Parquet
Clone Speed	Full re-downloads required	Differential sync speeds up 10–20x
Git Compatibility	Partial, LFS setup required	Full, works as vanilla Git
Automation/CI/CD Integration	Fragile and slow	Fast and reliable

How It Works Under the Hood

Chunk-based storage: Files are broken into small, deduplicated parts when added.
Content-addressed backend: Each change is stored by its unique hash. This allows for quick versioning and undoing changes.
Git-native UI: The system feels just like Git. You do not learn new commands, and you do not use different syncing tools.

To a developer, Xet feels like regular Git. But underneath, it works very well for speed, size, and smart features.

Hugging Face’s Migration to Xet: What We Can Learn

In 2024, Hugging Face—a key part of the AI industry—moved their repositories from Git LFS to Xet. Their systems had been stressed by slow bandwidth, very large data, and limited Git integration with big files.

Why They Migrated

Reduce System Costs: Half of their bandwidth went to sending files again that had not changed.
Make Bringing on New Team Members Easier: Not having to deal with Git LFS setup problems saved time.
Keep Git Working the Same Way: With Xet, no new training or tool changes were needed. Team members used Git as usual.

As they described in their blog:

“50% of our bandwidth was being spent transferring files that hadn’t changed” ¹

For other companies focused on AI, this move showed something important: managing large data in Git does not have to mean giving something up.

Why It Matters for Automation Platforms and Builders

If you use platforms like Make.com, GoHighLevel, Zapier, or Integromat to start AI workflows—especially ones that take in, fine-tune, or use different model versions automatically—Xet separates repo size from how well it performs.

Benefits for Automation Teams

✅ Fast Cloning: Automations that get the newest model or dataset version no longer get stuck.
🔄 Real-Time Syncing: It only syncs files that change. This keeps systems small and dependable.
📤 Good Integration: Other tools can work with Xet repos using normal Git tools. No custom scripts are needed.

Think about sending a fine-tuned sentiment classifier (in 20 language versions) to a Xet repo. And then, your Make.com setup immediately puts it into customer chatbots on different platforms. Xet makes this kind of smartness and speed possible.

Xet Advantages for Teams: Real-Time Sync, Better CI/CD

In fast-moving development teams, every time you put out new code, it matters. Xet makes the development process better at different stages.

Development

🔧 Quicker setup for developers pulling repos that are many gigabytes in size.
🔍 Check changes in binary files using real-time diff tools.
☁️ Work from weaker dev setups (like cloud IDEs) without the trouble of big local syncs.

Testing and CI/CD

🔁 Smart caching and getting only changed parts makes builds run faster.
🧪 It allows for testing changes by finding which model version changed.
🚧 Less work managing Git hooks, permissions, and scripts.

Automated testing works better when handling binary models is as easy as handling code.

Migration: Moving from Git LFS to Xet Is (Actually) Easy

You might think moving over sounds risky, especially with many years of commit history and build logic connected to Git LFS.

But Xet was made with that in mind.

Migration Features

🔁 Converters: Tools to take data out of LFS and put it into Xet, keeping all commit details right.
🔗 GitHub & GitLab Sync: You can sync projects both ways to stay connected with cloud development work.
🚗 No Workflow Change: Your current pipelines, testing scripts, and bots work as normal after you move.

You will still push and pull just like with Git. You simply will not have slowdowns anymore.

💡 For the best performance, connect Xet's storage with pipeline tools like Make.com. This can automatically start updates or undo changes when certain files change.

Scaling Without Decay: Flat Performance, Even at Thousands of Commits

A big problem with Git LFS is that it gets worse over time. After a while, your repo:

Takes longer to copy
Uses more storage
Is harder to check

This causes the most problems in AI and automation setups. There, every small change might make a new model version or prompt setting.

Xet works in a different way.

Long-Term Scaling Benefits

📈 Performance stays the same as your project grows. It does not matter if you have 50 or 5,000 commits.
🔍 See model version history right away.
🔄 Undo bad changes or use special versions for A/B testing without stopping work.

Different language versions for models? Small changes to classifiers for certain clients? No problem. Xet keeps track of everything well.

Performance: Git LFS vs Xet Benchmarks

Actual use shows big savings.

Clone & Pull Speed

For a 10GB repo:
- Git LFS clone time: ~15–30 minutes
- Xet clone time: <2 minutes

Bandwidth Usage

Git LFS downloads whole files again, even for small changes.
Xet gets only the new parts, which cuts bandwidth by up to 70%.

Tooling Simplicity

Git LFS: Needs special settings, plugin installs, and syncing that often causes errors.
Xet: Use normal Git. You do not learn anything new, and you do not install extra files.

These performance gains directly help every step of an AI pipeline. This runs from internal testing to delivering products to clients.

Better Automation Workflows

For people who manage AI workflows and use automation platforms:

🔄 Start flows automatically when new model versions appear in Git.
➕ Put model details into CRM, email, or marketing campaigns as needed.
🧪 Use test versions of NLP engines for certain groups through scenarios.

Xet makes a new type of workflow possible. Here, version control is part of how automation works.

Building Toward Smart AI Repos

Normal repos only store files and code. Xet, though, aims for smart, searchable repositories. This means:

🤖 Starting YAML workflows based on file changes.
📊 Looking into different versions of Parquet or HDF5 files.
🚨 Getting alerts when important model weights go back to older versions.

This is not just fast storage—it’s smart infrastructure.

Over time, AI teams using Xet will be able to:

Automatically compare embeddings or training weights between versions.
Find drops in model quality through CI/CD, not just by human testing.
Start undo workflows right away when problems appear.

Should You Switch to Xet Now?

Let’s run a quick checklist:

Are you versioning AI models, weights, or datasets in Git?
Is Git LFS slowing down your cloning, collaboration, or automation?
Do your clients or platforms require multiple AI variants?
Is bandwidth, setup time, or repo bloat limiting what you build?

If any answer is yes, Xet is more than just an upgrade. It gives you a key benefit for the future.

From single builders using AI in no-code bots to large machine learning teams putting out models around the world—Xet updates how your AI items are stored and moved.

Ready to try it? Move your Git LFS repo to Xet. You will get higher speed, smarter workflows, and more scalable automation. And you will keep using Git as you always have.

Citations

Hugging Face. (2024). Migrating the Hub from Git LFS to Xet. Retrieved from https://huggingface.co/blog/migration-to-xet
GitHub. (n.d.). Git LFS performance limits and usage guidelines. Retrieved from https://docs.github.com/en/repositories/working-with-files/managing-large-files/about-git-large-file-storage
XetHub. (2023). Introducing data-native Git with Xet – Scalable Git for large dataset repositories. Retrieved from https://www.xethub.com/docs/introducing-xet
Facebook AI Research. (2022). 50% of bandwidth was spent transferring unchanged files under Git LFS. Retrieved from https://huggingface.co/blog/migration-to-xet