Maximizing GPU Utilization with NVIDIA Run:ai and NVIDIA NIM

2026-02-27 · Source: NVIDIA Technical Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Advanced, medium

Summary

NVIDIA NIM and NVIDIA Run:ai address the challenge of low GPU utilization and high costs in LLM inference deployments by introducing intelligent scheduling strategies. NVIDIA NIM standardizes model deployment via containerized microservices with optimized inference engines, industry-standard APIs, and automatic model optimization. NVIDIA Run:ai complements this by providing advanced orchestration, including inference-first prioritization, GPU fractions with memory isolation, dynamic GPU fractions for variable traffic, and GPU memory swap for rarely-used models. Benchmarking shows significant improvements: ~2x GPU utilization with minimal throughput loss, up to ~1.4x higher throughput under heavy concurrency with dynamic fractions, and 44-61x faster first-request latency using GPU memory swap, consolidating workloads from three H100 GPUs to approximately 1.5 H100 GPUs.

Key takeaway

For Machine Learning Engineers managing LLM inference, adopting NVIDIA NIM with NVIDIA Run:ai's intelligent scheduling can drastically improve GPU utilization and reduce operational costs. You should evaluate implementing GPU fractions and dynamic memory management to handle diverse inference workloads efficiently, and consider GPU memory swap to eliminate cold-start penalties for less frequently accessed models, freeing up valuable GPU capacity.

Key insights

Intelligent orchestration with NVIDIA NIM and Run:ai significantly boosts GPU utilization and inference performance for LLMs.

Principles

• Prioritize inference workloads over training jobs.
• Isolate GPU memory for co-located models.
• Dynamically adjust GPU memory based on workload demand.

Method

Deploy containerized inference engines (NIM) with intelligent scheduling (Run:ai) using GPU fractions, dynamic memory allocation, and CPU-GPU memory swapping to optimize resource use.

In practice

• Use GPU fractions for small LLMs to consolidate workloads.
• Implement dynamic GPU fractions for variable, high-concurrency traffic.
• Employ GPU memory swap for infrequently accessed models to reduce cold-start latency.

Topics

• LLM Inference Optimization
• GPU Resource Scheduling
• NVIDIA NIM
• Dynamic GPU Fractions
• GPU Memory Swap

Best for: Machine Learning Engineer, NLP Engineer, CTO, MLOps Engineer, AI Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA Technical Blog.