Parallel Language Models promise 5.7x faster LLM inference at autoregressive quality
ArashVahdat · x · 2026-10-30
Researchers at UC Irvine with ETH collaborators introduce Parallel Language Models: predicting several tokens in parallel at autoregressive-level quality, scaled to 3B parameters. By adding a parallel decoder during pretraining, they report up to 5.7x inference speedup after post-training. The team says everything will be open-sourced.
Related event: Parallel Language Models Speed Up LLM Inference up to 5.7x(2 posts)→
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