Advances in Large-Scale Model Training and Deployment

Recent developments in the field of large-scale model training and deployment have focused on enhancing efficiency, scalability, and resource utilization. Key innovations include novel load-balancing methods for parallel training of Mixture of Experts (MoE) models, which aim to reduce communication costs and improve throughput. Additionally, advancements in post-training optimization after model pruning have introduced scaling laws to determine optimal post-training data usage, significantly reducing resource demands while maintaining model performance.

In the realm of inference, high-throughput systems for MoE models on memory-constrained GPUs have been proposed, leveraging pipelining and hierarchical resource management to achieve superior throughput. These systems not only optimize GPU memory usage but also support larger models on multiple GPUs, making advanced models more accessible.

Another notable trend is the integration of topology-aware scheduling for co-located large language model (LLM) workloads, which ensures efficient resource allocation and improves overall system performance. This approach addresses the challenges of heterogeneous workload priorities and varying resource requirements, enhancing the efficiency of preemptive scheduling.

Noteworthy Papers:

• Pro-Prophet: Introduces a systematic load-balancing method for efficient parallel training of large-scale MoE models, achieving significant speedup and load-balancing enhancement.
• MoE-Lightning: Proposes a high-throughput MoE batch inference system that significantly outperforms existing methods, achieving up to 10.3x higher throughput on resource-constrained GPUs.
• Topology-aware Preemptive Scheduling: Develops a fine-grained topology-aware method for preemptive scheduling of hybrid workloads, improving overall scheduled performance by 55%.