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UC San Diego's BeNice Slashes AI Memory Use by 66% in Looped Models
A new two-stage training recipe lets looped language models reuse a single KV-cache across every recurrence, cutting HBM use up to 66%.
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AlphaSignal
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AlphaSignal Newsroom
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A new two-stage training recipe lets looped language models reuse a single KV-cache across every recurrence, cutting HBM use up to 66%.
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