Sign up to save your podcasts
Or
Share [QA] Hogwild! Inference: Parallel LLM Generation via Concurrent Attention
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
[QA] 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
View all episodes
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
More shows like Arxiv Papers
View all
FT News Briefing
699 Listeners
Google DeepMind: The Podcast
200 Listeners
Last Week in AI
282 Listeners
Latent Space: The AI Engineer Podcast
76 Listeners
The AI Daily Brief (Formerly The AI Breakdown): Artificial Intelligence News and Analysis
443 Listeners