Description
Serving world models for video generation requires a different balance of latency and throughput than serving large language models. In this livestream, Nebius Physical AI team members Stewart Tong and Timothy Le examine NVIDIA Cosmos 3 Super benchmarks through vLLM-Omni on NVIDIA HGX B200 and HGX H200 systems.
Their work compares four serving topologies, ranging from one eight-GPU replica to eight single-GPU replicas. See how replica count, parallelism, and concurrency affect request latency and validated video-seconds per node-hour. Learn which settings to hold constant for video generation benchmarks and how to check generated clips for file integrity and basic motion.
What You'll Learn:
- Why faster video requests can mean less output per node
- How to measure latency and throughput with video-validation checks
- When more replicas help and extra concurrency adds delay
- What NVIDIA HGX B200 and HGX H200 benchmarks show about allocating GPUs
- Have questions about serving NVIDIA Cosmos 3 Super for world-model video generation? Drop them in live — the NVIDIA and Nebius teams will address them during the discussion.
Learn more about NVIDIA Cosmos 3 serving and reproduce the benchmark:
📄 Read the technical blog → https://nebius.com/blog/posts/serving-world-models-latency-node-throughput
🧪 Reproduce the benchmarks → https://github.com/nebius/nebius-physical-ai
📚 Explore the NVIDIA Cosmos platform → https://github.com/nvidia/Cosmos
👥 Join the NVIDIA Omniverse community → https://discord.com/invite/nvidiaomniverse