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Fast Inference from Transformers via Speculative Decoding
This paper introduces speculative decoding, a method to sample from autoregressive models faster by running them in parallel on the outputs of approximation models. It achieves 2X-3X acceleration without changing the distribution.
https://arxiv.org/abs//2211.17192
YouTube: https://www.youtube.com/@ArxivPapers
PODCASTS:
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
This paper introduces speculative decoding, a method to sample from autoregressive models faster by running them in parallel on the outputs of approximation models. It achieves 2X-3X acceleration without changing the distribution.
https://arxiv.org/abs//2211.17192
YouTube: https://www.youtube.com/@ArxivPapers
PODCASTS:
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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