为超大规模训练设计的高效通信框架，支持10万+GPU协同计算
Collective Communication for 100k+ GPUs
- 基于NCCLX框架优化跨集群通信，适配大模型全生命周期需求
- 在10万+GPU集群上实现高吞吐低延迟，Llama4训练效率显著提升
- 适合超大规模AI模型研发团队，尤其关注训练扩展性与稳定性
大型语言模型（LLMs）规模持续增长，要求高度高效的集体通信框架，尤其是在训练工作负载扩展至数十万甚至更多GPU时。传统通信方法在此规模下面临显著的吞吐量和延迟瓶颈，阻碍了前沿模型的研发与部署。本文提出由Meta开发的NCCLX集体通信框架，专为优化大模型全生命周期性能而设计，从大规模训练的同步需求到推理阶段的低延迟要求均能有效支持。该框架可支撑超过10万GPU的复杂集群工作负载，确保可靠、高吞吐、低延迟的数据交换。在Llama4模型上的实证评估表明，通信效率获得显著提升。本研究为下一代大模型在前所未有的规模上运行提供了稳健解决方案。
原文摘要 · Abstract (English)
The increasing scale of large language models (LLMs) necessitates highly efficient collective communication frameworks, particularly as training workloads extend to hundreds of thousands of GPUs. Traditional communication methods face significant throughput and latency limitations at this scale, hindering both the development and deployment of state-of-the-art models. This paper presents the NCCLX collective communication framework, developed at Meta, engineered to optimize performance across the full LLM lifecycle, from the synchronous demands of large-scale training to the low-latency requirements of inference. The framework is designed to support complex workloads on clusters exceeding 100,000 GPUs, ensuring reliable, high-throughput, and low-latency data exchange. Empirical evaluation on the Llama4 model demonstrates substantial improvements in communication efficiency. This research contributes a robust solution for enabling the next generation of LLMs to operate at unprecedented scales.
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