D3D: Decoupled Diffusion Drafting for Asynchronous Speculative Decoding
The paper introduces D3D, a standalone diffusion drafter for speculative decoding that removes dependence on target-model hidden states.
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- Primary sourceHugging Face
- Discovery linkHugging Face
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The paper introduces D3D, a standalone diffusion drafter for speculative decoding that removes dependence on target-model hidden states. This lets drafting of successor blocks overlap with target verification in a packed pass, enabling asynchronous speculative decoding. The authors report that removing five of 36 layers retains over 99% of mean acceptance length on three benchmarks with Qwen3-8B. Across seven benchmarks, they report average decoding speedups of 5.00x and 5.71x for Qwen3-8B and Qwen3-32B targets, versus 4.03x and 3.67x for DFlash, with up to 7.48x on GSM8K. Code is stated to be publicly available.
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Source claims not independently checked; speedup figures are the authors' own benchmark results, and the full article was not separately verified beyond the fetched page.
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D3D is a standalone diffusion drafter that removes target-model hidden-state dependencies, enabling asynchronous speculative decoding.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseLayer pruning removes five of 36 layers while retaining over 99% of mean acceptance length on three benchmarks with Qwen3-8B.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseReported average speedups across seven benchmarks are 5.00x (Qwen3-8B) and 5.71x (Qwen3-32B), versus 4.03x and 3.67x for DFlash.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseLimits from the stored brief
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Why this may matter
Speculative decoding speedups bear directly on LLM serving cost and latency. Decoupling drafting from target hidden states could let drafting overlap with verification, a design change that may matter for inference systems if the reported gains hold.
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