33 citations · 34 across the 4 of their papers we have counts for
4 papers
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…
Kernel Looping: Eliminating Synchronization Boundaries for Peak Inference Performance
David Koeplinger, Darshan Gandhi, Pushkar Nandkar +9
Token generation speed is critical to power the next wave of AI inference applications. GPUs significantly underperform during token generation due to synchronization overheads at…
SambaNova SN40L: Scaling the AI Memory Wall with Dataflow and Composition of Experts
Raghu Prabhakar, Ram Sivaramakrishnan, Darshan Gandhi +27
Monolithic large language models (LLMs) like GPT-4 have paved the way for modern generative AI applications. Training, serving, and maintaining monolithic LLMs at scale, however, r…
Training Large Language Models Efficiently with Sparsity and Dataflow
Venkat Srinivasan, Darshan Gandhi, Urmish Thakker +1
Large foundation language models have shown their versatility in being able to be adapted to perform a wide variety of downstream tasks, such as text generation, sentiment analysis…