EdgeLLM-Nano: auditing polar-guided key-cache compression and consistency-anchored pruning on a 33.6 MB on-device language model
Read the original at arxiv.org→arXiv:2610.14475v1 Announce Type: new Abstract: Deploying language models (LMs) on the edge presents unique memory, latency, and power challenges absent in high-performance computing. KV-cache compression emerges as...
Original headline: "EdgeLLM-Nano: Auditing Polar-Guided Key-Cache Compression and Consistency-Anchored Pruning on a 33.6 MB On-Device Language Model"
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- Oct 14, 04:00 UTC arXiv cs.CL lead source EdgeLLM-Nano: Auditing Polar-Guided Key-Cache Compression and Consistency-Anchored Pruning on a 33.6 MB On-Device Language Model