# [Proposal] Native bounce compression for Hugging Face Hub — 25% bandwidth savings for model downloads

> Source: <https://discuss.huggingface.co/t/proposal-native-bounce-compression-for-hugging-face-hub-25-bandwidth-savings-for-model-downloads/177012#post_1>
> Published: 2026-06-21 04:17:51+00:00

Problem Statement

Downloading large AI models (10–100 GB) from Hugging Face Hub is:

**Time-consuming** for users on slower connections
**Expensive** in terms of bandwidth costs for both HF and users
**Storage-intensive** for Hugging Face infrastructure

Current compression options (gzip, zstd) are **not optimized for neural network weights** (IEEE-754 float tensors).

Proposed Solution

Integrate **bounce compression** natively into Hugging Face Hub.

Key Benefits

- 25% average compression on model weights (
`.safetensors`

, `.pt`

, `.gguf`

)
- 1069 MB/s decompression speed — faster than most network connections
- Specialized for ML: byte-shuffle transform optimized for IEEE-754 tensors
- CRC-32 integrity verification built-in
- Zero dependencies: pure Rust, Apache-2.0 license

Benchmark: Safetensors Model Weights (255.5 MB)

| Tool |
Compressed Size |
Ratio |
Decompress Speed |
**bounce -2** |
**218.1 MB** |
**85.3%** |
**1069.0 MB/s** |
| zstd -3 |
235.3 MB |
92.1% |
1121.8 MB/s |
| gzip -9 |
235.6 MB |
92.2% |
492.9 MB/s |
| brotli -q 5 |
235.1 MB |
92.0% |
212.6 MB/s |

bounce saves 17.2 MB (7% better) than the next best tool while maintaining 5x faster decompression than gzip.

Proposed Integration

CLI

```
# Download with automatic decompression
huggingface-cli download model/name --compress bounce
```

Python SDK

``` python
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="model/name",
    filename="model.safetensors",
    compression="bounce"  # auto-decompress .bnc files
)
```

ROI for Hugging Face

**Storage Savings**: 25% reduction across millions of models (1 PB → 250 TB saved)
**Bandwidth Savings**: 25% less egress traffic, significant CDN cost reduction
**User Experience**: Faster downloads worldwide, lower data costs for metered connections

Resources

Open Questions

- Should this be opt-in or automatic for large files?
- Backward compatibility strategy for existing downloads?
- Integration timeline with
`huggingface_hub`

Python package?

I am happy to collaborate on implementation — bounce is production-ready, well-tested, and designed specifically for this use case.