DEER proposes diffusion drafts to accelerate LLM inference
A publication dated December 18, 2025 describes DEER, which drafts tokens with diffusion models and verifies them with autoregressive models. The text claims up to 5.54× faster inference without quality loss.
Source: DEER Usa Rascunhos de Diffusion para Inferência de LLMs (x.com). Text prepared with AI from this source.
What happened and what to do
On December 18, 2025, the publication described DEER, an approach that uses diffusion models to draft tokens and autoregressive models to verify them. The text claims inference speedups of up to 5.54× without quality loss and presents the method as an alternative to speculative decoding techniques.
A company could test the approach on its models and workloads, measuring latency, throughput, resource use, and response quality. A project could instrument inference, compare configurations in a controlled environment, and present the results in a dashboard to support an adoption decision.
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Wendelmaques can assess the opportunity for your inference workload, define an evaluation scope, and implement instrumentation, tests, and a comparison dashboard. Operations and next steps can be scoped in the proposal.
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