5 papers · 1 filter
MoEs Are Stronger than You Think: Hyper-Parallel Inference Scaling with RoE
Soheil Zibakhsh, Mohammad Samragh, Kumari Nishu +3
The generation quality of large language models (LLMs) is often improved by utilizing inference-time sequence-level scaling methods (e.g., Chain-of-Thought). We introduce hyper-par…
Mirror Speculative Decoding: Breaking the Serial Barrier in LLM Inference
Nikhil Bhendawade, Kumari Nishu, Arnav Kundu +3
Speculative decoding accelerates LLM inference by using a draft model to look ahead, but gains are capped by the cost of autoregressive draft generation: increasing draft size elev…
MoE-PHDS: One MoE checkpoint for flexible runtime sparsity
Lauren. A Hannah, Soheil Zibakhsh, Kumari Nishu +4
Sparse Mixtures of Experts (MoEs) are typically trained to operate at a fixed sparsity level, e.g. in a top- gating function. This global sparsity level determines an operat…
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…
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…