---
id: hf-transfer
version: "0.1.9"
license: unclear
license_treatment: unclear
maintenance: active
---

# hf-transfer — Speed up file transfers with the Hugging Face Hub.

License: unclear · Maintenance: active · Downloads: 5.0M/mo

## What it is and what it does

hf-transfer is a Rust-based acceleration layer for file transfers with the Hugging Face Hub. It replaces the default Python I/O backend to saturate very high-bandwidth network connections—specifically targeting scenarios where Python's GIL and native I/O cannot fully utilize available bandwidth. The package is explicitly a power-user tool: it lacks progress bars, minimal error reporting, and is designed for environments where network speed is the primary constraint, not user experience.

You enable it by setting the environment variable `HF_HUB_ENABLE_HF_TRANSFER=1` before importing the consuming tool. Once active, any upload or download through the Hub will use hf-transfer's compiled backend. The package has no runtime dependencies and ships as precompiled wheels for most CPU architectures and Python versions from 3.7 onward, making installation straightforward on supported platforms.

Use it for:

- Downloading large model weights or datasets on institutional or cloud networks with multi-gigabit connectivity
- Uploading large model checkpoints or datasets from high-bandwidth environments where Python I/O is the bottleneck
- Batch processing of many large files where cumulative transfer time is significant and network saturation is achievable
- Research or production pipelines on dedicated high-speed infrastructure where transfer speed directly impacts iteration time

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Accelerates file transfers to and from the Hugging Face Hub by using a compiled backend optimized for high-bandwidth networks, bypassing Python's speed limitations.

Yes, if you regularly transfer large files on very high-bandwidth networks and have confirmed that Python I/O is your bottleneck. Set `HF_HUB_ENABLE_HF_TRANSFER=1` and it works transparently. No, if you are on typical internet connections, have limited bandwidth, or need detailed progress reporting—the overhead of compilation is not justified and the tool deliberately lacks user-facing features. Verify the unclear license status before use in proprietary contexts.

## Install

pip install hf-transfer
uv add hf-transfer
poetry add hf-transfer

## Installing hf-transfer

Before you install: Medium install friction due to compiled wheels; however, prebuilt binaries are available for common platforms (x86_64, ARM, PowerPC) and Python versions from 3.7 onward. The project is actively maintained with a recent release on 2025-01-07.

License in practice: License status is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify the repository directly before adopting in proprietary or restricted-license contexts.

Quickstart:

pip install hf-transfer

import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"

This package is a backend accelerator with no runtime dependencies; it only functions when imported by another tool that recognizes the HF_HUB_ENABLE_HF_TRANSFER environment variable. Only beneficial on very high-bandwidth networks where native I/O is the bottleneck.

Verify before relying:

- Exact bandwidth threshold or performance gains compared to standard transfers
- Whether this package works with all versions of tools that consume it or has specific constraints
- Compatibility with alternative Hub implementations or mirrors beyond the official Hub

## Package facts

- License: not declared (unclear)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 5.0M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

hugging face hub file transfer speed, fast model download upload, high bandwidth file transfer, hub acceleration, speed up transfers, performance-optimization, rust-backend

[View on SkillFed](https://skillfed.io/packages/hf-transfer) · [View on PyPI](https://pypi.org/project/hf-transfer/)