Scaling efficient production-grade inference with NVIDIA Run:ai on Nebius

As AI's role in production grows, inference has emerged as a crucial operational challenge, with demands for continuous scalability directly affecting cost and user experience. NVIDIA emphasizes that throughput, latency, and cost per token are now core business metrics, as inference workloads have evolved to include combinations of large language models, embedding models, and task-specific models. Traditional GPU deployment methods, which dedicate full GPUs to individual models, lead to inefficiencies and rising costs. To address this, NVIDIA and Nebius conducted benchmarks using NVIDIA Run:ai on Nebius AI Cloud to test fractional GPU allocation. The results demonstrated improved efficiency and scalability for real-world inference workloads, with consistent throughput scaling, enhanced GPU utilization, stable latency, and reliable elastic autoscaling across multi-model workloads. This approach, utilizing dynamic workload scheduling and fractional GPU allocation, offers a more efficient model for production inference environments, reducing idle capacity and maintaining performance under high concurrency.

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