6 papers · 1 filter
Structuring The Future: Diffusion LLM Speculative Decoding via Calibrated Draft Graphs
Sudhanshu Agrawal, Risheek Garrepalli, Raghavv Goel +3
Diffusion LLMs (dLLMs) have recently emerged as a powerful alternative to autoregressive LLMs (AR-LLMs) with the potential to operate at significantly higher token-generation rates…
QUOKA: Query-Oriented KV Selection For Efficient LLM Prefill
Dalton Jones, Junyoung Park, Matthew Morse +3
We present QUOKA: Query-oriented KV selection for efficient attention, a training-free and hardware agnostic sparse attention algorithm for accelerating transformer inference under…
Double-P: Hierarchical Top-P Sparse Attention for Long-Context LLMs
Wentao Ni, Kangqi Zhang, Zhongming Yu +7
As long-context inference becomes central to large language models (LLMs), attention over growing key-value caches emerges as a dominant decoding bottleneck, motivating sparse atte…
Fast Forward: Accelerating LLM Prefill with Predictive FFN Sparsity
Aayush Gautam, Mukul Gagrani, Junyoung Park +3
The prefill stage of large language model (LLM) inference is a key computational bottleneck for long-context workloads. At short-to-moderate context lengths (1K--16K tokens), Feed-…
CAOTE: KV Cache Selection for LLMs via Attention Output Error-Based Token Eviction
Raghavv Goel, Junyoung Park, Mukul Gagrani +5
While long context support of large language models has extended their abilities, it also incurs challenges in memory and compute which becomes crucial bottlenecks in resource-rest…
Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs
Raghavv Goel, Mukul Gagrani, Wonseok Jeon +3
Text generation with Large Language Models (LLMs) is known to be memory bound due to the combination of their auto-regressive nature, huge parameter counts, and limited memory band…