Efficient Generative Inference for Large Transformer Models

This research focuses on improving the efficiency of generative inference for Transformer models, specifically in large deep models with tight latency targets and long sequence lengths. The study develops an analytical model for inference efficiency and combines it with low-level optimizations to achieve a new Pareto frontier on the latency and model FLOPS utilization tradeoffs on 500B+ parameter models. With appropriate partitioning, it enables scaling up to 32x larger context lengths and achieves a low-batch-size latency of 29ms per token during generation.

This research provides insights into improving the efficiency of generative inference for large Transformer models, which can be useful for businesses looking to implement AI in their processes and workflows. It recommends using multi-dimensional partitioning techniques optimized for TPU v4 slices based on the application requirements and incorporating low-level optimizations to achieve better tradeoffs between latency and model FLOPS utilization. The value of this research lies in enabling businesses to develop AI models that can efficiently process larger context lengths and generate output with lower latency.