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Hogwild! Inference: Parallel LLM Generation via Concurrent Attention
This paper presents Hogwild! Inference, a parallel LLM inference engine enabling LLMs to collaborate effectively using a shared attention cache, enhancing reasoning and efficiency without fine-tuning.
https://arxiv.org/abs//2504.06261
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
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 presents Hogwild! Inference, a parallel LLM inference engine enabling LLMs to collaborate effectively using a shared attention cache, enhancing reasoning and efficiency without fine-tuning.
https://arxiv.org/abs//2504.06261
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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