Accelerator-Optimized Infrastructure for Training and Serving Next-Generation Multimodal Foundation Models
Abstract
The rapid convergence of vision, language, audio, and structured-data modalities into unified foundation models has exposed a widening gap between the computational assumptions of first-generation transformer training stacks and the heterogeneous, bursty, and memory-diverse workloads that multimodal architectures actually generate. This paper presents a comprehensive systems study of accelerator-optimized infrastructure spanning silicon-level design considerations, interconnect topology, distributed training runtimes, and disaggregated serving architectures purpose-built for next-generation multimodal foundation models. We characterize the arithmetic-intensity and memory-bandwidth profile of representative multimodal kernels, propose a reference architecture that unifies tensor, pipeline, sequence, and data parallelism with modality-aware scheduling, and evaluate an end-to-end optimization pipeline across eight infrastructure interventions. Our experiments, conducted on clusters ranging from 8 to 2048 accelerators, show that a fully optimized stack achieves up to 61% model FLOPs utilization, a 63% reduction in training cost per million tokens, and up to 3.8x energy efficiency relative to an unoptimized baseline, while serving-side disaggregation of prefill and decode phases reduces median per-token latency by 45% at high concurrency. We conclude with a discussion of open challenges in heterogeneous accelerator fleets, cross-modal load balancing, and the co-design of hardware roadmaps with multimodal model architectures
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