Characterizing and Efficiently Accelerating Multimodal Generation Model Inference

Generative artificial intelligence (AI) technology is revolutionizing the computing industry, posing new system design and optimization opportunities. In particular, AI’s ability to understand and respond in multiple modalities comes with significant system resource demands. To sustainably scale generative AI capabilities to billions of users in the world, inference must be fast and efficient. This article pinpoints key system design and optimization opportunities by characterizing a family of emerging multimodal generation models on real systems. Autoregressive token generation is a critical latency performance bottleneck, typically dominated by GPU idle time. In addition to memory-intensive attention across the generative AI models, linear operations constitute significant inference latency due to the feed forward networks in Transformer-based models. We demonstrate that state-of-the-art optimization levers, spanning from applications to system software and hardware, set a 3.88× better baseline.

Keywords:

Generative AI

Computational modeling

Translation

Artificial intelligence

Decoding

Graphics processing units

Codes

Vocoders

Transformers

Memory management

Characterizing and Efficiently Accelerating Multimodal Generation Model Inference

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