Multimodal and Distributed LLMs: Bridging Scalability and Cross-Modal Reasoning

Large Language Models (LLMs) have emerged as a cornerstone of modern artificial intelligence, achieving remarkable capabilities in natural language understanding and generation. As their scale and utility have increased, two critical and complementary trends have defined their evolution: (1) the distributed systems and algorithms enabling efficient training of ultra-large models across massive compute infrastructures, and (2) the integration of multiple modalities—such as vision, audio, and structured data—into unified multimodal large language models (MLLMs). This survey provides a comprehensive examination of the state-of-the-art in both of these dimensions.We begin by exploring the foundations and advances in distributed training, including model parallelism, pipeline parallelism, memory optimization strategies, and the design of sparse and expert models. We assess system-level techniques such as ZeRO, DeepSpeed, and tensor sharding that allow for scalable, memory-efficient training at trillion-parameter scale. Next, we turn to multimodality, surveying architectures and training objectives that extend LLMs to process and generate across diverse input types. We review contrastive learning, cross-attention fusion, and aligned token embeddings as key techniques that enable cross-modal reasoning, with illustrative examples from models like Flamingo, CLIP, and GPT-4V.Beyond current methodologies, we identify and formalize the core technical challenges facing distributed and multimodal LLMs, including memory bottlenecks, communication overhead, alignment in the absence of ground truth, robustness to modality shifts, and evaluation under open-ended tasks. To guide future research, we outline six key directions: unified memory-augmented architectures, modular and composable systems, self-aligning mechanisms, lifelong and continual learning agents, embodied multimodal cognition, and the emergence of general-purpose foundation agents.Our goal is to synthesize recent progress while articulating a vision for the next generation of foundation models—models that are not only scalable and multimodal but are also capable of reasoning, grounding, and adapting to complex, real-world environments. This survey serves both as a technical reference and a roadmap for researchers and practitioners navigating the future of large-scale, multimodal, and distributed AI systems.