Innovation Insight for GenAI Infrastructure

Description

Figure 1: AI Factory — The Platform for Scaling AI Value

Layer 1: The Physical Foundation — AI-Optimized Infrastructures

Compute Accelerators

• On-premises options
  • Incumbent general-purpose GPUs: Deploy for maximum R&D and product velocity and diverse workloads, where attracting talent via a mature software ecosystem is critical.
  • Challenger general-purpose GPUs: Target for massive, single-model training, where novel memory architectures can reduce infrastructure complexity and latency.
  • AI accelerators: Use to slash TCO and power consumption for high-volume, static inference workloads like recommendation engines.
• Cloud and hosted options
  • Hyperscaler-native silicon: Leverage for massive-scale inference and, when relevant for training, to optimize TCO when already strategically committed to a single hyperscaler’s platform.
  • Certified colocation providers: Utilize to gain hardware control and meet data residency needs without the capital expenditure of a full data center retrofit.

High-Performance Networking

• On-premises options
  • Scale-out:
    • Lossless proprietary fabrics (such as InfiniBand): Deploy to minimize job completion time in large-scale training clusters where performance is the primary driver, accepting ecosystem dependency for operational simplicity, low latency and performance.
    • Open-standard Ethernet (such as RDMA over Converged Ethernet [RoCEv2] and Ultra Ethernet Consortium [UEC-enhanced]): Implement to maintain vendor diversity and leverage existing talent, accepting a higher initial tuning burden (especially for emerging UEC-like standards) for long-term flexibility and cost control (see Critical Capabilities for Data Center Switching).
    • Data processing unit (DPU)-augmented Ethernet: Use to offload network and security tasks from host CPUs, maximizing compute cycles for the primary AI workload, especially in multitenant environments.
  • Scale-up:
    • High-speed proprietary interconnects (such as NVIDIA’s NVLink): Use to train and inference the largest foundation models, where multiple accelerators must function as a single, unified processor with maximum bandwidth.
    • Open-standard interconnects (such as UALink): Architect for future-state flexibility, derisking long-term strategy by preventing lock-in to a single vendor’s accelerator ecosystem.
• Cloud and hosted options
  • Scale-out:
    • Cloud-native fabrics (such as Amazon Web Services [AWS] Elastic Fabric Adapter [EFA] and Google Jupiter): The back-end fabric follows the accelerator, even in the cloud. Leverage cloud-native fabrics for hyperscaler silicon like tensor processing units (TPUs) and AWS’ Trainium, but expect to use InfiniBand for large-scale NVIDIA GPU clusters, tying your fabric choice directly to the accelerator ecosystem.
    • All of the scale-up and scale-out options of on-prem are also available selectively in the cloud environments.
  • Scale-out and scale-up in colocation:
    • Utilize to gain control of on-prem hardware without the massive capital expenditure (capex) and time required for a full data center retrofit, ideal for regulated industries.

Storage Infrastructure Designed for AI

• On-premises
  • High-throughput parallel file systems: Deploy as the training all-flash “hot tier” to deliver maximum throughput, keeping expensive GPU and ASIC clusters fully saturated with data by eliminating storage bottlenecks.
  • All-flash distributed file systems and/or object storage platforms: Use as the core data lake for data preparation and mixed I/O workloads, engineered to handle billions of small files with high performance.
  • Specialized AI data integrated platforms: Implement for MLOps-driven environments where data governance, versioning and workflow automation are critical for reproducibility and compliance.
• Cloud and hosted options
  • Cloud-native high-performance storage: Leverage for cloud-based training, providing a managed, high-throughput file system that integrates seamlessly with a hyperscaler’s compute services.
  • Cloud object storage as the foundation: Architect as the cost-effective, massively scalable persistence layer, serving as the gravity center for your entire hybrid AI data fabric.

Layer 2: AI Platform Orchestration and Governance

The Elastic Resource Orchestration Core

• Cloud-native orchestration (such as Kubernetes): Use as the universal control plane across all environments (including on-prem, cloud and edge). This provides the standard, portable API for managing all containerized GenAI workloads and is the foundation upon which all other tools are built.

• Advanced AI orchestrators (such as those with NVIDIA Run:ai-like features): Layer on top of your Kubernetes foundation to enable dynamic sharing of accelerator clusters. This is critical for maximizing utilization by using queuing and fractionalization to run mixed training and inference workloads, a capability you can deploy in both your on-prem data centers and cloud instances.
• High-performance computing (HPC) schedulers (such as SchedMD’s Slurm): Integrate within your Kubernetes environment to provide raw throughput for massive, tightly coupled foundation model training. This allows you to run traditional HPC-style workloads on any infrastructure, whether it’s your own bare-metal hardware or a fleet of powerful cloud virtual machines (VMs).

• Advanced inference engines (such as vLLM, SGLang, NVIDIA Dynamo and Google’s Pathways): Mandate these runtimes on your chosen infrastructure to slash the cost-per-token for production inference. Whether running on-prem for data privacy or in the cloud for scale, their novel memory techniques dramatically increase GPU throughput for the high-concurrency RAG and conversational AI applications that define modern GenAI.

Platform Governance, Security and FinOps

• Policy-as-code enforcement: The key is to enforce identical, auditable security policies across your entire hybrid estate. This means a single security posture can automatically prevent a noncompliant container from running, whether it’s on an on-prem, cloud-based or hosted AI cluster, eliminating manual, location-specific reviews.
• Unified identity and access management: This becomes the system of record for attributing every action to an identity (human or machine), regardless of where the compute occurs. This unbreakable chain of custody is essential for debugging a hybrid agentic workflow or proving data residency during an audit, even when a job uses both on-prem and cloud resources.

• Federated cluster management: The nonobvious goal is to enable strategic, cross-platform workload execution. A researcher should be able to submit a massive training job that starts on the on-prem Slurm cluster and, when resources are saturated, automatically bursts the overflow to a managed Kubernetes service in the public cloud, all managed as a single, seamless workflow.
• Holistic observability and tracing: This is no longer for debugging a single application; it’s for securing the entire, distributed AI supply chain. A unified observability stack must trace a problematic AI output back through a cloud-hosted inference API to the specific on-prem AI cluster where the model was last fine-tuned and to the data source it was trained on.

• AI FinOps and cost governance: This evolves from cost control into profit and loss (P&L) management for the entire hybrid AI factory. The platform must provide a single, unified view of costs, showing a business unit a single bill that combines the amortized cost of its on-prem GPU usage with the consumption-based cost of its cloud-based inference calls, enabling true, all-in ROI calculations for AI projects.

Layer 3: Powering AI as a Service

XOps for Applied AI

• Self-service portals and tooling: Deploy as the AI factory’s unified front door, giving data scientists self-service access to compute and tooling to boost productivity and reduce I&O’s operational burden.
• Automated pipeline and workflow engines: Use as the assembly line for model development, automating the MLOps life cycle to enforce governance, ensure reproducibility and accelerate innovation.
• Distributed AI runtimes: Deploy as the core scaling engine to run a single training job across a massive accelerator grid, essential for efficiently training foundation models.

• Production model serving and management: Leverage Kubernetes-native tools to manage production deployment, enabling sophisticated traffic management like canary rollouts and A/B testing for safe, monitored rollouts at scale.
• Fully managed MLOps platforms: Adopt a hyperscaler’s end-to-end platform for the fastest time to market. This low-friction, integrated solution is ideal for teams focused on models over infrastructure, at the cost of deep platform commitment.

Focus on the “As-a-Service” Outcome

• On-premises and hybrid cloud service offerings
  • On-demand training environments: Use platform blueprints to offer self-service, on-prem training environments for open-weight models. This gives teams the freedom to fine-tune models on sensitive corporate data with maximum privacy and control.
  • Internal RAG and inference services: Offer one-click templates that deploy open-weight models for internal RAG or inference. This is ideal for cost-effective, high-volume tasks where the data is private and the enterprise is willing to assume full liability for the model’s output.
• Cloud and hosted enablement
  • Frontier model gateway: For external-facing applications, use the federated platform to provide secure, audited access to cloud-exclusive frontier models (such as from Google, OpenAI and Anthropic). This is the only path to leverage provider-backed liability protection and intellectual property (IP) indemnification, a critical requirement for managing enterprise risk.
  • AI agent and reasoning workflows: Abstract the complexity of agentic workflows by creating templates that chain together multiple services. A single request from a developer could deploy a powerful, liability-protected reasoning model in the cloud for complex planning, while simultaneously calling a cost-effective, on-prem task model for data processing, all managed and orchestrated through the central platform.