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Weaver Boosts LLM Speculative Decoding Speed by Over 4x

Yuma Oda, Ryan Mathieu, Roman Knyazhitskiy, Artur Chakhvadze· July 9, 2026 View original

Summary

Researchers introduce Weaver, an autoregressive adapter that constructs proposal trees from factorized drafter marginals, significantly enhancing speculative decoding for large language models. This method restores conditional dependencies between proposed tokens, leading to a 4.37-fold speedup over standard autoregressive decoding and outperforming DFlash.

Speculative decoding is a technique that accelerates autoregressive language models by generating multiple tokens in a single forward pass, improving interactivity. Factorized draft models are particularly efficient for this, as they predict future token probabilities in parallel. However, their assumption of independence between tokens causes performance to drop as the speculative budget increases. To overcome this limitation, a new approach called Weaver has been developed. Weaver is a lightweight autoregressive adapter that builds proposal trees using the top-K marginals from a factorized drafter. This design reintroduces conditional dependencies between the proposed tokens without needing a full-vocabulary projection. Combined with a rollback-free tree-verification algorithm and optimized CUDA kernels in SGLang, Weaver achieves substantial speed improvements. It delivers a 4.37-fold speedup compared to standard autoregressive decoding and outperforms the highly optimized DFlash baseline by 24.7%.

Why it matters

This advancement significantly improves the inference speed and interactivity of large language models, making them more practical and responsive for real-time applications and user-facing products.

How to implement this in your domain

  1. 1Evaluate current LLM inference latency for your applications.
  2. 2Explore integrating speculative decoding techniques like Weaver into your LLM serving infrastructure.
  3. 3Benchmark Weaver's performance against existing decoding methods on your specific models and hardware.
  4. 4Consider contributing to or adopting open-source implementations of Weaver for faster deployment.

Who benefits

AI/ML DevelopmentSoftware EngineeringCloud ComputingContent CreationCustomer Service

Key takeaways

  • Weaver significantly speeds up LLM speculative decoding.
  • It addresses limitations of factorized draft models by restoring conditional dependencies.
  • The method achieves over 4x speedup compared to standard decoding.
  • Optimized CUDA kernels contribute to its high performance.

Original post by Yuma Oda, Ryan Mathieu, Roman Knyazhitskiy, Artur Chakhvadze

"arXiv:2607.06763v1 Announce Type: new Abstract: Speculative decoding greatly increases the interactivity of autoregressive language models by trading off computation for extra tokens generated in a single forward pass. Factorized draft models are especially efficient because they…"

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Originally posted by Yuma Oda, Ryan Mathieu, Roman Knyazhitskiy, Artur Chakhvadze on X · view source

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