As organizations move toward deploying AI models in production, the demand for private, secure, and scalable inference infrastructure is growing. The NVIDIA “Reference Design for Private AI Inference Using HGX Servers” offers a comprehensive architecture blueprint for building performant inference clusters tailored to enterprise needs.
What’s Inside the Reference Design?
1. Purpose of the Architecture
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Deliver fast, private AI inference across diverse workloads.
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Provide a blueprint optimized for NVIDIA HGX systems using the latest H100 GPUs.
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Address challenges such as data privacy, latency, model scaling, and operational efficiency.
2. Hardware Foundation: NVIDIA HGX Platform
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Based on the NVIDIA HGX system with 8x H100 GPUs interconnected using NVLink.
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Supports PCIe Gen5, 4th Gen Intel Xeon Scalable CPUs, and high-throughput networking via NVIDIA ConnectX-7 and BlueField DPUs.
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Designed for high-density data centers with liquid cooling and multi-node scale-out.
3. Key Software Components
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NVIDIA Triton Inference Server for scalable deployment of AI models.
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Kubernetes-based orchestration using NVIDIA AI Enterprise and NGC Helm charts.
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MIG (Multi-Instance GPU) partitioning for workload isolation and density optimization.
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Monitoring and observability tools like DCGM and Prometheus.
4. Performance Benchmarks
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Up to 9x increase in inference throughput with H100 over A100 (using GPT-J, ResNet50, etc.).
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Reduced latency and improved GPU utilization through MIG and TensorRT optimizations.
5. Use Cases
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Generative AI (e.g., LLMs, diffusion models)
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Vision-based applications (e.g., OCR, object detection)
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Speech and audio AI
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Healthcare, financial services, retail, and government workloads that demand on-prem privacy
6. Deployment Best Practices
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Use Kubernetes node pools for GPU and non-GPU workloads.
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Enable NUMA-aware scheduling and GPU pinning.
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Integrate inference with enterprise monitoring and CI/CD pipelines.
Why It Matters
This reference architecture helps enterprises build AI inference infrastructure that:
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Keeps sensitive data on-prem.
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Reduces reliance on public clouds.
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Achieves predictable latency and throughput for mission-critical AI applications.
Get the Full PDF
You can download the full official NVIDIA reference design here:
👉 Download Reference Design for Private AI Inference (PDF)
