Question 1

What is sovereign compute?

Accepted Answer

Sovereign compute is GPU and AI infrastructure that an organization owns, operates, and controls within its own facilities. Unlike cloud compute where GPU resources are rented from providers like AWS, Azure, or GCP, sovereign compute means the physical hardware, the software stack running on it, and all data processed by it are under your organization's direct control. This includes NVIDIA DGX systems, GPU-equipped servers, networking, storage, and the orchestration software that manages AI workloads. Sovereign compute is essential for organizations that can't send data to cloud providers due to regulatory requirements, security classifications, or strategic decisions about AI infrastructure ownership.

Question 2

How do you manage GPU clusters for AI workloads?

Accepted Answer

Managing GPU clusters for AI workloads requires orchestration software that handles resource allocation, job scheduling, multi-tenant isolation, and utilization monitoring. The Enfuse platform integrates with tools like Run:ai for GPU virtualization and Kubernetes for container orchestration, enabling dynamic GPU allocation across multiple AI workloads. Key management capabilities include: fractional GPU sharing (running multiple smaller models on a single GPU), GPU pooling across nodes, priority-based scheduling for different teams or applications, real-time utilization monitoring, and automated scaling based on demand. Without proper orchestration, enterprises typically achieve only 20-30% GPU utilization—proper management pushes this to 70-90%.

Question 3

Should I build or buy sovereign GPU infrastructure?

Accepted Answer

The build vs. buy decision depends on three factors: (1) Scale—if you need more than 8-16 GPUs sustained, building is almost always more economical over a 3-year horizon. Below that, cloud may be more cost-effective unless compliance requirements mandate on-prem. (2) Expertise—building requires GPU infrastructure expertise for hardware selection, networking (InfiniBand/RoCE), cooling, and ongoing operations. If you don't have this expertise, a managed service model (like Enfuse provides) gives you sovereign infrastructure without building an ops team. (3) Timeline—cloud GPUs can be provisioned in minutes, while on-prem hardware procurement takes 8-16 weeks. If you need immediate capacity, cloud can bridge the gap while sovereign infrastructure is being deployed.

Question 4

What GPUs are best for on-prem LLM inference?

Accepted Answer

For production LLM inference, NVIDIA H200 GPUs offer the best performance-per-dollar for most enterprise workloads. They provide 141GB HBM3e memory—enough to run 70B parameter models on a single GPU without quantization. For smaller deployments, the DGX Spark brings desktop-class AI compute for 7B-70B models. For large-scale multi-model serving, DGX H200 systems with 8 GPUs provide enough memory and compute for 405B parameter models. For organizations planning ahead, NVIDIA Blackwell (B200/GB200) offers 2-3x performance improvement for inference workloads. The right choice depends on model sizes, concurrent users, latency requirements, and budget. Our architecture team provides detailed hardware recommendations based on your specific workload profile.

Question 5

How do you optimize GPU utilization for AI?

Accepted Answer

Most organizations waste 60-80% of their GPU investment through poor utilization. Optimization strategies include: (1) GPU virtualization—using tools like Run:ai to partition GPUs and run multiple workloads on shared hardware; (2) Model optimization—using TensorRT-LLM, quantization (INT8/INT4), and KV-cache optimization to reduce memory footprint and increase throughput; (3) Intelligent scheduling—routing inference requests to the smallest capable model for each task, reserving large GPUs for complex reasoning; (4) Batch inference—grouping similar requests to maximize GPU throughput; (5) Right-sizing—matching GPU hardware to actual workload requirements rather than over-provisioning. The Enfuse runtime handles most of these optimizations automatically, providing built-in model routing, batch management, and utilization monitoring.

Question 6

What does sovereign compute cost compared to cloud GPU?

Accepted Answer

Cloud GPU costs range from $2-8 per GPU-hour for H100-class hardware, scaling to $15,000-50,000+ per month for sustained single-GPU usage. Sovereign compute requires upfront investment: a DGX H200 system (8 GPUs) costs approximately $300,000-500,000 including networking and installation. However, the per-GPU-hour effective cost drops dramatically over time—typically reaching $0.50-1.50 per GPU-hour within the first year and continuing to decrease as the hardware amortizes over 3-5 years. At sustained usage of 50+ GPU-hours per day, sovereign compute achieves 40-60% lower TCO within 18-24 months. Additional cost considerations include power/cooling ($0.10-0.30 per GPU-hour), facility space, and operations staffing (or managed service fees). We provide detailed TCO modeling during architecture consultation.

Feature	Sovereign AI	Cloud AI
GPU Availability Guaranteed access to GPU resources	Dedicated, always available	Shared, may face shortages
Data Location Physical location of compute infrastructure	Your facility	Provider's datacenters
Cost Model How you pay for compute	Fixed infrastructure cost	Variable per-hour pricing
TCO at Scale (>100 GPUs) Total cost of ownership trajectory	40-60% lower over 3 years	Higher at sustained usage
Customization Ability to customize the compute stack	Full hardware/software control	Limited to provider options
Compliance Regulatory compliance coverage	Inherits your facility controls	Limited to provider certifications
Capacity Planning How capacity is managed	Requires forecasting	On-demand scaling
Operational Expertise Skills needed to operate	Required (or managed service)	Managed by provider

Sovereign Compute: GPU Infrastructure for On-Premises AI

Why Own Your GPU Infrastructure?

Predictable Economics

Guaranteed Capacity

Complete Control

GPU Hardware for Sovereign AI

Desktop / Edge

Enterprise Inference

Training & Scale

The GPU Utilization Problem

GPU Virtualization

Intelligent Routing

Model Optimization

Utilization Monitoring

Sovereign Compute vs Cloud GPU: Cost Analysis

The TCO Crossover

Continue Learning

On-Prem LLM Deployment

Air-Gapped AI Platform

What Is Sovereign AI?

Private GenAI Infrastructure

Agents are an infrastructure workload.

Sizing and serving

Isolation and capacity

Operate and recover

One platform across GKE, Azure, Azure Local, and your own racks.

Hybrid inference fabric

Google Kubernetes Engine (GKE)

Azure, AKS, and Azure Local

Private infrastructure

Kubernetes as the common layer

vLLM and open models

Enterprise networking and security

Hybrid inference routing

Frequently Asked Questions

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