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

A proposal to integrate bounce compression natively into Hugging Face Hub promises 25% bandwidth savings for model downloads, with benchmarks showing 7% better compression than zstd and 5x faster decompression than gzip. The solution targets neural network weights and could save 250 TB of storage across the platform.

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.