NVIDIA Run:ai Enables Massive Token Throughput via GPU Fractioning

NVIDIA Run:ai, in collaboration with Nebius AI Cloud, has demonstrated significant improvements in LLM inference performance through dynamic GPU fractioning. The benchmarking results indicate that fractional GPUs can dramatically increase effective capacity without compromising latency SLAs. This is achieved by intelligently scheduling and dynamically allocating GPU resources, allowing enterprises to run multiple LLMs on the same GPUs and scale resources based on workload demands.

The key benefits of this approach include increased throughput, efficient resource utilization, and predictable latency. By enabling near-linear throughput scaling across different GPU fractions, NVIDIA Run:ai helps organizations maximize GPU ROI and reduce infrastructure costs. The platform also addresses the challenges of manually managing GPU allocation and scaling LLMs, providing an elastic environment where GPUs can be repurposed during off-peak hours.

The implications of this technology are far-reaching. As AI workloads continue to scale, dynamic GPU fractioning will become a foundational capability for running large-scale, multimodel LLM inference efficiently in production. This will enable enterprises to deploy AI-powered applications more cost-effectively and accelerate the development of new AI services. The partnership between NVIDIA and Nebius AI Cloud provides a flexible, production-ready framework for organizations looking to leverage the benefits of dynamic GPU fractioning.