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2 papers published across 1 lab.
Full-attention LLMs are intrinsically sparse and can be transformed into highly efficient sparse models with minimal training, sidestepping the need for expensive sparse pre-training.
Forget static KV cache compression – KVServe dynamically adapts compression strategies to your service context, slashing latency by up to 32.8x in disaggregated LLM serving.
