视频生成也能像直播一样流畅？新系统省37%成本
你刷短视频时，每个视频片段都是AI实时生成的——但背后有个大麻烦：AI模型一旦开始生成，就不能随便停，否则会断片。TurboServe是第一个专门为这种“流式视频生成”设计的调度系统。它像交通指挥一样，把不同用户的生成任务动态分配到GPU上，空闲时自动缩减GPU数量省电，忙时快速扩容。在64块B300 GPU的集群上，最慢的片段生成时间缩短了37.5%，总GPU成本降低了37.2%。这不是你明天能用的工具，但它是让AI视频生成服务更便宜、更流畅的关键技术。
📄 原文摘要(英文)
Streaming video generation is emerging as a new serving workload in which users interact with long-lived sessions that generate video progressively, chunk by chunk. Unlike offline video generation or typical LLM serving, streaming video generation must preserve session state across active and idle periods, repeatedly schedule ongoing sessions, and deliver each chunk under a tight latency target. This creates two key serving challenges in multi-user, multi-GPU environments: session duration heterogeneity, where long-running sessions make placement decisions suboptimal over time, and temporal user-demand heterogeneity, where the number of active sessions fluctuates sharply across bursts and idle periods. We present TurboServe, the first serving system designed specifically for streaming video generation workloads. TurboServe formulates serving as an online scheduling problem that jointly coordinates session placement and GPU provisioning. Its closed-loop scheduling algorithm combines a migration-aware placement controller, which rebalances sessions across GPUs to reduce the maximum per-chunk latency, with a load-driven autoscaling controller, which adapts the GPU budget to workload variation for improved cost efficiency. To support these decisions at runtime, TurboServe implements coalesced chunk processing for batching concurrent active sessions on the same GPU, GPU-CPU offloading for session suspension and resumption, and NCCL-based GPU-GPU migration for online rebalancing. We evaluate TurboServe on real-world production traces from Shengshu Technology across multiple model sizes and GPU clusters with up to 64 NVIDIA B300 GPUs. Compared with baseline serving configurations, TurboServe reduces worst-case per-chunk latency by 37.5% and total GPU operating cost by 37.2% on average. Our code is publicly available at https://github.com/shengshu-ai/TurboServe.