3 papers
cs.AI2026
SnapStream: Efficient Long Sequence Decoding on Dataflow Accelerators
Jonathan Li, Nasim Farahini, Evgenii Iuliugin +19
The proliferation of 100B+ parameter Large Language Models (LLMs) with 100k+ context length support have resulted in increasing demands for on-chip memory to support large KV cache…
cs.CL2026
Cross-Family Speculative Prefill: Training-Free Long-Context Compression with Small Draft Models
Shubhangi Upasani, Ravi Shanker Raju, Bo Li +5
Prompt length is a major bottleneck in agentic large language model (LLM) workloads, where repeated inference steps and multi-call loops incur substantial prefill cost. Recent work…
cs.CL2025
LLMs Know What to Drop: Self-Attention Guided KV Cache Eviction for Efficient Long-Context Inference
Guangtao Wang, Shubhangi Upasani, Chen Wu +5
Efficient long-context inference is critical as large language models (LLMs) adopt context windows of ranging from 128K to 1M tokens. However, the growing key-value (KV) cache and…