Parallel Language Models Speed Up LLM Inference up to 5.7x
A UC Irvine and ETH team introduced Parallel Language Models (PLM), which use variational decoding with latent auxiliary variables to predict multiple tokens in a single forward pass, speeding up LLM inference by up to 5.7x without sacrificing autoregressive-level quality.
2026-10-30 ~ 2026-10-30 · 2 related posts
- Parallel Language Models paper: multi-token generation at AR quality, up to 5.7x speedup — aaron_lou · 2026-10-30
- Parallel Language Models promise 5.7x faster LLM inference at autoregressive quality — ArashVahdat · 2026-10-30