Prefilling-dLLM: Predictive Prefilling for Long-Context Inference in Diffusion Language Models
arXiv:2606.10537v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) re-encode the entire prefix at every denoising step, causing recomputation that scales quadratically with context length and becomes prohibitive for long-context scenarios. We propose Prefilling-dLLM, a training-free prefill-decode disaggregation framework for dLLMs that partitions the prefix into N chunks, caches their KV representations once, and selects the top-K most relevant chunks with intra-chunk token sparsity for decoding, showing that sparse prefilling can outperform dense attention while reducing
The increasing demand for long-context capabilities in large language models has exposed the quadratic scaling recomputation problem inherent in current diffusion models, necessitating new efficiency paradigms.
This development addresses a fundamental efficiency bottleneck in diffusion large language models, potentially enabling more powerful and cost-effective long-context AI applications.
Diffusion LLMs can now process significantly longer contexts more efficiently, reducing computational costs and opening doors for applications previously deemed prohibitive.
- · AI model developers
- · Cloud computing providers
- · Enterprises using long-context AI
- · AI hardware manufacturers
- · Competitors with less efficient long-context solutions
Reduced computational cost for long-context diffusion LLMs leads to broader adoption and deployment of these models.
New AI applications become feasible, particularly in areas like complex document analysis, scientific discovery, and advanced code generation.
Increased demand for specialized AI hardware optimized for sparse attention mechanisms and KV caching, potentially reshaping chip development priorities.
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Read at arXiv cs.CL