5 papers
ITME: Inference Tiered Memory Expansion with Disaggregated CXL-Hybrid Memories
Hakbeom Jang, Younghoon Min, Sunwoong Kim +5
The rapid shift toward agentic and long-context workloads in Large Language Models (LLMs) is pushing the industry beyond the capacity of individual servers toward disaggregated sha…
EpiCache: Episodic KV Cache Management for Long-Term Conversation on Resource-Constrained Environments
Minsoo Kim, Arnav Kundu, Han-Byul Kim +2
Modern large language models (LLMs) extend context lengths to millions of tokens, enabling coherent, personalized responses grounded in long conversational history. However, the Ke…
TIDE: Every Layer Knows the Token Beneath the Context
Ajay Jaiswal, Lauren Hannah, Han-Byul Kim +3
We revisit a universally accepted but under-examined design choice in every modern LLM: a token index is looked up once at the input embedding layer and then permanently discarded.…
MemoryLLM: Plug-n-Play Interpretable Feed-Forward Memory for Transformers
Ajay Jaiswal, Lauren Hannah, Han-Byul Kim +4
Understanding how transformer components operate in LLMs is important, as it is at the core of recent technological advances in artificial intelligence. In this work, we revisit th…
SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models
Han-Byul Kim, Duc Hoang, Arnav Kundu +2
With the rapid expansion in the scale of large language models (LLMs), enabling efficient distributed inference across multiple computing units has become increasingly critical. Ho…