NVIDIA Run:ai GPU Fractioning Delivers 77% Throughput at Half Allocation
Darius Baruo Feb 18, 2026 18:31
NVIDIA and Nebius benchmarks show GPU fractioning achieves 86% user capacity on 0.5 GPU allocation, enabling 3x more concurrent users for mixed AI workloads.
NVIDIA's Run:ai platform can deliver 77% of full GPU throughput using just half the hardware allocation, according to joint benchmarking with cloud provider Nebius released February 18. The results demonstrate that enterprises running large language model inference can dramatically expand capacity without proportional GPU investment.
The tests, conducted on clusters with 64 NVIDIA H100 NVL GPUs and 32 NVIDIA HGX B200 GPUs, showed fractional GPU scheduling achieving near-linear performance scaling across 0.5, 0.25, and 0.125 allocations.
Hard Numbers from Production Testing
At 0.5 GPU allocation, the system supported 8,768 concurrent users while maintaining time-to-first-token under one second—86% of the 10,200 users supported at full allocation. Token generation hit 152,694 tokens per second, compared to 198,680 at full capacity.
Smaller models pushed these gains further. Phi-4-Mini running on 0.25 GPU fractions handled 72% more concurrent users than full-GPU deployment, achieving approximately 450,000 tokens per second with P95 latency under 300 milliseconds on 32 GPUs.
The mixed workload scenario proved most striking. Running Llama 3.1 8B, Phi-4 Mini, and Qwen-Embeddings simultaneously on fractional allocations tripled total concurrent system users compared to single-model deployment. Combined throughput exceeded 350,000 tokens per second at full scale with no cross-model interference.
Why This Matters for GPU Economics
Traditional Kubernetes schedulers allocate whole GPUs to individual models, leaving substantial capacity stranded. The benchmarks noted that even Qwen3-14B, the largest model tested at 14 billion parameters, occupies only 35% of an H100 NVL's 80GB capacity.
Run:ai's scheduler eliminates this waste through dynamic memory allocation. Users specify requirements directly; the system handles resource distribution without preconfiguration. Memory isolation happens at runtime while compute cycles distribute fairly among active processes.
This timing coincides with broader industry moves toward GPU partitioning. SoftBank and AMD announced validation testing on February 16 for similar fractioning capabilities on AMD Instinct GPUs, where single GPUs can split into up to eight logical devices.
Autoscaling Without Latency Spikes
Nebius tested automatic scaling with Llama 3.1 8B configured to add GPUs when concurrent users exceeded 50. Replicas scaled from 1 to 16 with clean ramp-up, stable utilization during pod warm-up, and negligible HTTP errors.
The practical implication: enterprises can run multiple inference models on existing GPU inventory, scale dynamically during peak demand, and reclaim idle capacity during off-hours for other workloads. For organizations facing fixed GPU budgets, fractioning transforms capacity planning from hardware procurement into software configuration.
Run:ai v2.24 is available now. NVIDIA plans to discuss the Nebius implementation at GTC 2026.
Image source: Shutterstock- nvidia
- gpu
- ai infrastructure
- llm inference
- run:ai
