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vllm - 💡(How to fix) Fix [Feature]: Implement EDEN over TurboQuant (better performance and more accurate attribution) [1 participants]

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vllm-project/vllm#41551•Fetched 2026-05-04 04:58:56
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🚀 The feature, motivation and pitch

You're already working towards a TurboQuant implementation. I think you'll find EDEN (NeurIPS 2021, ICML 2022) to be a bit too similar, easier to implement, and more performant (saving an entire bit per coordinate in the unbiased case).

Please see the following article for an overview: https://towardsdatascience.com/how-a-2021-quantization-algorithm-quietly-outperforms-its-2026-successor/ .

Disclaimer: I am the author of the linked article and a co-author of the original EDEN papers (NeurIPS 2021, ICML 2022). I am happy to answer any questions regarding this situation or the underlying math.

Alternatives

Alternative solution? Getting used to the fact that people are using EDEN without optimal scaling under a new name, five years after the original publication.

Additional context

DRIVE: One-bit Distributed Mean Estimation (2021), NeurIPS 2021

EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning (2022), ICML 2022.

A Note on TurboQuant and the Earlier DRIVE/EDEN Line of Work (2026), arXiv:2604.18555.

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extent analysis

TL;DR

Consider implementing the EDEN algorithm as a potentially more performant and easier-to-implement alternative to TurboQuant.

Guidance

  • Review the EDEN papers (NeurIPS 2021, ICML 2022) and the article on Towards Data Science to understand the algorithm and its benefits.
  • Compare the implementation complexity and performance of EDEN with TurboQuant to determine the best approach for your use case.
  • Evaluate the trade-offs between using an existing, well-performing algorithm like EDEN versus investing in a newer, potentially less mature solution like TurboQuant.

Notes

The issue lacks specific technical details about the current implementation or requirements, so a more detailed solution cannot be provided.

Recommendation

Apply workaround: Implement the EDEN algorithm as it seems to be a more established and performant solution.

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vllm - 💡(How to fix) Fix [Feature]: Implement EDEN over TurboQuant (better performance and more accurate attribution) [1 participants]