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Many enterprise GPUs run a single model during inference — even when it uses only ~30% of memory.
So how much capacity is being left on the table?
In our latest benchmark with @nebiusai, we used NVIDIA Run:ai fractional GPU allocation and NVIDIA NIM to measure real-world impact on throughput, latency, and concurrency.
What we found:
✅ 86% of full GPU capacity using just a 0.5 slice
✅ 3× more users with mixed workloads on shared GPUs
✅ Near-linear scaling down to 0.125 slices
✅ Zero latency cliffs during autoscaling
Stop GPU fragmentation. Start maximizing throughput.
🔗 Read the full deep dive: nvda.ws/40gIlsj

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