NVIDIA has introduced a new feature in TensorRT-LLM called multiblock attention, which significantly improves AI inference throughput by up to 3.5x on the HGX H200 platform. This innovation tackles the challenges of long-sequence lengths, as seen in modern generative AI models such as Llama 2 and Llama 3.1.
These models have larger context windows, enabling them to perform complex cognitive tasks over extensive datasets. However, this expansion presents challenges in AI inference, such as low-latency demands and small batch sizes. NVIDIA’s TensorRT-LLM multiblock attention solves these issues by distributing computational tasks across all available SMs, maximizing GPU resource utilization and improving overall system throughput.
