At Meta, we are revolutionizing large language model (LLM) inference systems, optimizing applications like the Meta AI App through advanced parallelism techniques. Our focus is on enhancing key performance metrics: resource efficiency, throughput, and latency. By maximizing GPU utilization and reducing response times, we ensure a seamless user experience. Our LLM inference involves two stages: the compute-bound prefill, where input prompts generate key-value caches, and the memory-bound decoding, which produces tokens one at a time.
To tackle engineering challenges, we employ tensor, context, and expert parallelism. Innovations like Direct Data Access (DDA) algorithms significantly reduce latency, achieving notable speedups compared to traditional methods. Our context parallelism optimizes for long inputs, allowing efficient processing of millions of tokens. Looking ahead, we aim for N-D parallelism to balance resources across heterogeneous hardware, addressing future challenges like optimizing cloud infrastructure and integrating communication for enhanced efficiency. These efforts are key to advancing AI applications globally.
Source link
