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Most teams running LLM inference are overprovisioning GPUs “just in case.” We benchmarked what happens when you combine NVIDIA NIM with NVIDIA Run:ai with the same inference workloads on dramatically fewer GPUs — with minimal throughput loss. How? ✅ GPU Fractions + Bin Packing: Run multiple NIM models on shared GPUs with true memory isolation — consolidate your deployments, increase density, and reduce your compute footprint. ✅ Dynamic GPU Fractions: Let models burst into unused headroom during traffic spikes — no extra GPUs required. ✅ GPU Memory Swap: Keep infrequently used models warm in CPU memory to slash first-request latency. ✅ Inference Priority Scheduling: Prioritize user-facing inference while training flexes around it. The takeaway: You may not need more GPUs — just smarter orchestration of the ones you have. 🔗 Full benchmarks and methodology in the tech blog: https://t.co/01rLJYI7wD