Sign up to save your podcasts
Or
Share [QA] xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
[QA] xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference
The xLSTM 7B model offers fast, efficient inference for LLMs, achieving competitive performance while significantly improving speed and efficiency compared to existing models like Llama and Mamba.
https://arxiv.org/abs//2503.13427
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
The xLSTM 7B model offers fast, efficient inference for LLMs, achieving competitive performance while significantly improving speed and efficiency compared to existing models like Llama and Mamba.
https://arxiv.org/abs//2503.13427
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
701 Listeners
Google DeepMind: The Podcast
200 Listeners
Last Week in AI
290 Listeners
Latent Space: The AI Engineer Podcast
76 Listeners
The AI Daily Brief (Formerly The AI Breakdown): Artificial Intelligence News and Analysis
442 Listeners