#Latency

Use llm-analysis for precise latency and memory estimation in Large Language Models (LLMs). This tool assists in configuring models, GPUs, data types, and parallelism to achieve an optimal setup, enhancing system performance. Assess different batch sizes, parallelism methods, and hardware adjustments to understand their effect on performance. Employ the LLMAnalysis class or command line interface for thorough analysis, aimed at improving insight and decision-making in LLM implementations.

FlatFormer enhances 3D point cloud transformer efficiency with flattened window attention, solving latency challenges for applications like autonomous driving. It reduces processing overhead by grouping point clouds equally and applying self-attention, achieving significant speed improvements over SST and CenterPoint while ensuring high accuracy on the Waymo Dataset. It delivers real-time performance on edge GPUs, surpassing traditional sparse convolutional methods in speed and performance on large-scale benchmarks.