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Fiodar Kazhamiaka on Muck Rack

Fiodar Kazhamiaka

As seen in: Joule
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Articles

Energy use of AI inference, efficiency pathways, and test-time scaling

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By Felipe Oviedo, Fiodar Kazhamiaka, Esha Choukse, Allen Kim, Amy Luers, Melanie Nakagawa, Ricardo Bianchini, Juan M. Lavista Ferres
| Joule Verified
Keywords artificial intelligence inference large language models energy efficiency data-center power artificial intelligence energy Introduction Large language model (LLM) inference is one of the fastest-growing sources of computing demand, with energy use already approaching that of web search and other mature digital services.1,2,3 As a result, many stakeholders are seeking reliable figures on how much energy is consumed during inference at a granular level (e.g., energy per textual query)...
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Octopus: Scalable Low-Cost CXL Memory Pooling

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By Yuhong Zhong, Pantea Zardoshti, Shuwei Teng, Fiodar Kazhamiaka
| arxiv.org
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