๐ฉNVIDIA Developer BlogโขStalecollected in 31m
Boost GPU Use with Run:ai & NIM
#gpu-scheduling#llm-inferencenvidia-run:ai-&-nimnvidiarun:ainim
๐กFix LLM inference GPU waste: Run:ai + NIM packs diverse models for max utilization.
โก 30-Second TL;DR
What Changed
Diverse LLM inference needs cause average GPU underutilization.
Why It Matters
Enterprises scaling LLM inference can achieve higher ROI on GPU investments by minimizing idle resources. This is vital amid surging AI compute demands and cost pressures.
What To Do Next
Trial NVIDIA Run:ai to schedule NIM containers for your LLM inference workloads.
Who should care:Enterprise & Security Teams
Key Points
- โขDiverse LLM inference needs cause average GPU underutilization.
- โขRun:ai enables dynamic scheduling and GPU fractionation for mixed workloads.
- โขNIM provides optimized containers for seamless model deployment.
- โขCombination reduces compute costs and latency variability.
๐ง Deep Insight
Background and context from public sources โ not the original article. 9 sources cited.
๐ Enhanced Key Takeaways
- โขNVIDIA NIM microservices are continuously updated with optimized inference engines that boost performance on the same infrastructure over time, enabling organizations to extract more value from existing GPU investments without hardware upgrades[5].
- โขRun:ai integration with NVIDIA NIM enables model profile selection that optimizes for either latency or throughput, with quantized profiles (e.g., fp8) preferred to reduce memory usage and enhance performance across diverse inference workloads[1].
- โขRed Hat OpenShift AI deployment of NVIDIA NIM provides serverless efficiency through built-in Knative integration for 'scale-to-zero' functionality, ensuring expensive GPU resources are only consumed when active requests hit NIM endpoints, directly addressing cost optimization[3].
- โขNVIDIA NIM supports multiple optimized inference engines including TensorRT-LLM, vLLM, and SGLang, allowing organizations to select the optimal runtime engine based on their specific NVIDIA-accelerated infrastructure configuration[5].
๐ ๏ธ Technical Deep Dive
- โขNIM model profiles set compatible model engines and criteria for engine selection, including precision levels, latency optimization, throughput optimization, and GPU requirements[1].
- โขNVIDIA NIM microservices expose industry-standard APIs for easy integration with enterprise systems and applications, scaling seamlessly on Kubernetes to deliver high-throughput, low-latency inference[5].
- โขRun:ai inference workloads include specifications for container images, datasets, network settings, and resource requests required to serve models, with workloads assigned to projects and affected by project quotas[1].
- โขRed Hat OpenShift AI deployment of NVIDIA NIM utilizes Kserve as InferenceServices with NVIDIA GPU Operator for hardware acceleration, and includes Service Mesh (Istio) for secure, encrypted communication between applications and AI models[3].
- โขNVIDIA NIM supports hardware profiles that standardize GPU access, ensuring NIMs are scheduled on nodes with correct CUDA versions and memory capacity[3].
๐ฎ Future ImplicationsAI analysis grounded in cited sources
GPU utilization rates will become a primary competitive differentiator for enterprise AI infrastructure vendors
As organizations deploy diverse LLM workloads with varying resource requirements, the ability to dynamically pack and schedule these workloads efficiently will directly impact total cost of ownership and ROI on GPU investments.
Quantized model profiles (fp8) will become the default deployment standard for cost-sensitive inference workloads
Search results indicate quantized profiles are preferred to reduce memory usage and enhance performance, suggesting industry-wide adoption will accelerate as enterprises prioritize cost reduction over marginal accuracy gains.
โณ Timeline
2025-02
Red Hat and NVIDIA announce AI Factory platform combining Red Hat AI Enterprise with NVIDIA AI Enterprise for enterprise deployments
2026-02
VAST Data announces support for NVIDIA NIM microservices deployment across CNode-X infrastructure with production-ready DataEngine blueprints
๐ Sources (9)
Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.
- docs.run.ai โ Nim Inference
- run-ai-docs.nvidia.com โ Integrations
- docs.nvidia.com โ Deploy Nvidia Nim Redhat
- blocksandfiles.com โ 4092639
- NVIDIA โ Nim Microservices
- nebius.com โ Running Nvidia Nim and Blueprint on Nebius AI Cloud
- techtarget.com โ Red Hat Nvidia Tighten Integration with AI Factory
- NVIDIA โ AI
- adtmag.com โ Red Hat and Nvidia Launch Coengineered AI Factory Platform for Enterprise Deployments
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Original source: NVIDIA Developer Blog โ
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