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Another brilliant release from @Nvidia Star Attention, a novel mechanism for LLM inference that achieves up to 11x speedup while maintaining 95-100% accuracy through a block-sparse approximation approach. Addresses the critical challenge of processing long sequences… https://t.co/nehlPL56gX
NVIDIA AI Research Unveils ‘Star Attention’: A Novel AI Algorithm for Efficient LLM Long-Context Inference https://t.co/bWeJvTWueA #NVIDIA #AIResearch #StarAttention #MachineLearning #LongContextInference #ai #news #llm #ml #research #ainews #innovation #artificialintelligenc… https://t.co/ywUm6T1LAH
Star Attention: Efficient LLM Inference over Long Sequences NVIDIA researchers introduce Star Attention, a two-phase attention mechanism that processes long sequences by combining blockwise-local attention for context encoding with sequence-global attention for query processing… https://t.co/e2pP2yyaSw
NVIDIA has introduced a new AI algorithm called Star Attention, designed to enhance the efficiency of long-context inference in large language models (LLMs). This innovative mechanism employs a two-phase attention approach that combines blockwise-local attention for encoding context with sequence-global attention for query processing. The algorithm reportedly achieves up to an 11x speedup while maintaining an accuracy rate of 95-100% through a block-sparse approximation method. Additionally, NVIDIA's Hymba model is noted for setting new standards in small language models, outperforming all sub-2 billion parameter models and rivaling the Llama-3.2-3 billion model. Furthermore, TensorRT-LLM's Multiblock Attention has demonstrated a threefold increase in throughput for long-sequence AI inference on NVIDIA HGX H200 GPUs, enhancing performance without the need for additional hardware.
