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Speculative decoding

PARD

PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model Adaptation

Superseded baseline#35 of 151 most-superseded · first seen Apr 23, 2025

Superseded — cited as a baseline and beaten by newer methods

2 papers critique it · 1 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites PARD as a baseline.

However, both methods face scalability challenges when training on long sequences.
— P-EAGLE: Parallel-Drafting EAGLE with Scalable Training
“PARD an2025pardacceleratingllminference trains small autoregressive models to mimic diffusion-style parallel generation, and then perform speculative decoding for target LLMs. However, the resulting small models lack the modeling capacity of the target LLMs, leading to limited acceptance lengths and a speedup ceiling of approximately 3×”
— DFlash: Block Diffusion for Flash Speculative Decoding

Beaten on benchmarks

Head-to-head results where a newer method reports beating PARD. Values are copied from the source paper's tables — verify against the cited paper.

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.