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

As AI moves into production, inference is becoming more and more a defining operational challenge. Training is finite; inference runs continuously, scales with real user demand and directly determines both cost and user experience. NVIDIA has emphasized this shift, highlighting how throughput, latency and cost per token are now core business metrics for production AI — not just technical considerations.

At the same time, inference workloads have fundamentally changed. Production environments rarely serve a single model in isolation. They combine large language models, embedding models and smaller task-specific models, often under bursty and highly concurrent demand. Traditional deployment patterns — dedicating full GPUs to individual models — lead to idle capacity, rising costs and unpredictable performance as workloads diversify.

What the benchmarks showWhat the benchmarks show

To evaluate a more efficient approach, NVIDIA and Nebius ran joint benchmarks using NVIDIA Run:ai, the AI workload orchestration and optimization software platform in Nebius AI Cloud. The goal was simple: test whether fractional GPU allocation could improve efficiency and scalability for real-world inference workloads — without compromising performance.

The results validate that it can. Across full GPUs and fractional slices down to 0.125 GPU, the benchmarks demonstrated:

• Consistent throughput scaling across single-node and multi-GPU clusters

• Improved GPU utilization with minimal idle capacity

• Stable latency, including Time-to-First-Token (TTFT), under mixed workloads and high concurrency

• Reliable elastic autoscaling behavior during scale-out events

Embedding models, in particular, performed well under high-density fractionalization, making them strong candidates for cost-sensitive and high-concurrency inference environments.

NVIDIA Run:ai on NebiusNVIDIA Run:ai on Nebius

Nebius AI Cloud is the foundation for production inference, delivering full-stack, scalable NVIDIA compute, networking and storage designed for predictable performance under real-world concurrency and mixed workloads. NVIDIA Run:ai layers intelligent GPU orchestration on top, using fractional GPU allocation and dynamic workload scheduling to maximize utilization while maintaining stable latency and throughput. Unlike static GPU partitioning, fractional GPUs are allocated and scheduled dynamically based on workload demand.

Together, Nebius and NVIDIA Run:ai deliver a more efficient model for scaling inference in production across fractional GPUs bringing improved utilization with minimal idle capacity, stable latency under high concurrency and reliable autoscaling behavior across multi-model workloads.

For a deeper technical breakdown of the benchmarking methodology and performance results, read the full analysis on the NVIDIA Developer Blog. Or check out the NVIDIA GTC 2026 session S82234, “Scale Inference Using Open Models: How Nebius Token Factory Delivers Control and Efficiency,” presented by Nebius.