# NCCLZ: Compression-Enabled GPU Collectives with Decoupled Quantization and Entropy Coding

arXiv: https://arxiv.org/abs/2605.12396 · HTML: https://mlsanity.com/paper/2605.12396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 13 of 13)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [SGLang: Efficient Execution of Structured Language Model Programs](https://mlsanity.com/paper/2312.07104/) (2023)
- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)
- [Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters](https://mlsanity.com/paper/1706.03292/) (2017)
- [Is Network the Bottleneck of Distributed Training?](https://mlsanity.com/paper/2006.10103/) (2020)
- [Demystifying NCCL: An In-depth Analysis of GPU Communication Protocols and Algorithms](https://mlsanity.com/paper/2507.04786/) (2025)
- [cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data](https://mlsanity.com/paper/2007.09625/) (2020)
- [Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees](https://mlsanity.com/paper/2507.08784/) (2025)
- [Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2404.06114/) (2024)
- [gZCCL: Compression-Accelerated Collective Communication Framework for GPU Clusters](https://mlsanity.com/paper/2308.05199/) (2023)
- [Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression](https://mlsanity.com/paper/2407.04272/) (2024)
- [TAH-QUANT: Effective Activation Quantization in Pipeline Parallelism over Slow Network](https://mlsanity.com/paper/2506.01352/) (2025)

## Abstract (arXiv)

Collective communication is a major bottleneck for multi-node GPU workloads in scientific computing and distributed deep learning, especially when inter-node bandwidth is limited. Although NCCL provides optimized GPU-centric collectives, large messages can still dominate end-to-end performance. Existing compression-enabled collective libraries either rely on MPI-based stacks that cannot fully exploit NCCL, omit entropy coding, or tightly couple full compressors with communication primitives, limiting compression ratio, flexibility, and communication-computation overlap. This paper presents NCCLZ, a compression-enabled GPU collectives that decouples quantization and entropy coding and integrates them at different layers of the stack. NCCLZ places quantization at the interface, embeds entropy coding into NCCL primitives, uses a lightweight device-side selector to choose coding strategies, and overlaps compression with communication to reduce exposed overhead. Experiments on scientific datasets, training gradients, and synthetic workloads show up to 9.65x speedup over NCCL and up to 3.34x improvement over prior compression-assisted collective libraries.