TL;DR
Enterprise AI does not become production-ready when a model endpoint responds or a GPU cluster comes online. It becomes production-ready when data intake, preparation, model evaluation, artifact promotion, deployment, security, observability, rollback, and retirement operate as one controlled lifecycle.
VMware Cloud Foundation 9.1 can provide much of the infrastructure substrate beneath that lifecycle through vSphere, vSAN, NSX, VCF Automation, VCF Operations, and integration with private AI services. It does not eliminate the need for data governance, model ownership, evaluation standards, service-level objectives, cost controls, or human approval. The real AI foundry is the operating model that connects those responsibilities.
Introduction
Many enterprise AI diagrams begin too high in the architecture. They show a chatbot, an intelligent application, or an autonomous workflow and then draw a simple arrow toward a model. That view is easy to understand, but it hides nearly everything required to operate AI safely and repeatedly.
The supplied image reverses the perspective. Healthcare, manufacturing, financial services, retail, engineering, and public-sector outcomes appear above ground. Beneath them sits a foundry that ingests data, prepares it, trains or adapts models, validates artifacts, controls promotion, serves inference, and observes the resulting services. VMware Cloud Foundation forms the platform layer beneath those activities, while segmented security zones protect the transitions between them.
That is a more useful enterprise mental model. The AI service visible to a user is only the top floor. Underneath it is a production system that must coordinate infrastructure, data, security, model lifecycle, application delivery, governance, and day-two operations.
This article translates the image into a practical VCF 9.1 operating model. The objective is not to treat every label in the illustration as a literal product component. The objective is to identify what an enterprise AI foundry must control, where VMware Cloud Foundation fits, and where additional organizational and technical responsibilities remain.
The Foundry Is an Operating Model, Not a GPU Room
The word foundry is useful because it implies repeatable production rather than one-time experimentation. A physical foundry does not merely provide heat. It controls inputs, stages, tolerances, quality gates, output specifications, safety systems, and waste. An enterprise AI foundry needs the same discipline.
A GPU cluster may supply accelerated compute, but accelerated compute alone does not answer the questions that determine whether an AI service is fit for production:
- Which data may enter the workflow?
- Who owns the resulting model, prompt, retrieval index, and policy bundle?
- Which evaluation results are required before promotion?
- Which identities may call the service?
- Which tools and data systems may the model reach?
- How are cost, latency, quality, drift, and policy violations measured?
- What triggers rollback, restriction, retraining, or retirement?
A useful foundry model therefore treats the entire AI lifecycle as a governed production chain:
- Inputs are governed. Data sources have owners, classifications, retention requirements, and permitted uses.
- Stages are controlled. Preparation, training, evaluation, promotion, and deployment occur inside defined trust and ownership boundaries.
- Artifacts are versioned. Models, prompts, retrieval indexes, policies, runtime images, and evaluation evidence are traceable.
- Capacity is scheduled. GPU, CPU, memory, storage, and network resources are allocated according to workload priority and service requirements.
- Outputs are measured. Technical health, model behavior, policy compliance, cost, and business outcomes are observed together.
- Defects trigger action. The operating model defines when to investigate, restrict, roll back, retrain, or retire an AI service.
VMware Cloud Foundation can standardize much of the infrastructure and service delivery underneath those controls. It cannot decide what constitutes an acceptable business outcome or responsible model behavior. Those remain enterprise decisions.
Assumptions and Scope
This article uses the image as a conceptual architecture rather than as an exact representation of a Broadcom reference design. The discussion assumes:
- VMware Cloud Foundation 9.1 is the private-cloud foundation.
- Enterprise workloads include a mixture of virtual machines, Kubernetes-based services, and supporting platform components.
- Accelerated compute is available for selected training, fine-tuning, embedding, or inference workloads.
- Identity, certificate, DNS, time, backup, data, and security services are treated as enterprise dependencies.
- The organization wants repeatable private AI delivery rather than a collection of isolated laboratories.
- Infrastructure telemetry will be combined with runtime, model, application, security, and business telemetry.
The design does not assume that every model must be trained on-premises. Enterprises may consume externally developed models, use hosted model APIs, fine-tune approved models privately, or operate hybrid retrieval and inference patterns. The same lifecycle controls still apply because data, identity, policy, cost, evaluation, and evidence must follow the service wherever its components run.
From Business Demand to a Production AI Service
The image presents seven foundry stages: ingestion, preparation, model training, validation, registry, deployment, and observability. In practice, those stages should be connected to an explicit business and governance intake process.
The following diagram shows the control chain that should exist between an idea and a production AI service:
The important detail is the right-hand control record at every stage. Production maturity depends on preserving evidence about ownership, purpose, versions, approvals, expected behavior, and operating conditions. Without that evidence, the lifecycle becomes a sequence of loosely connected technical tasks.
The final feedback path is equally important. Observability should not end with dashboards. It should produce a decision: continue operating, adjust capacity, change a prompt, refresh an index, investigate a regression, restrict access, roll back a release, or retire the service.
Translating the Image into Platform Responsibilities
The illustration uses industrial labels such as data refinery, GPU furnaces, model shaping, inference engines, and observability hub. Those metaphors can be translated into practical enterprise responsibilities.
| Image layer | Practical meaning | Primary controls | Failure when ignored |
|---|---|---|---|
| Data sources | Applications, documents, sensors, logs, transactions, APIs, devices, and external feeds | Ownership, classification, consent, lineage, retention, access | Unapproved or low-quality data enters production workflows |
| Data intake and refinery | Collection, normalization, filtering, enrichment, chunking, labeling, and feature preparation | Data contracts, schema checks, malware inspection, quality thresholds, secrets | Models receive inconsistent, stale, poisoned, or improperly exposed data |
| GPU furnaces | Accelerated compute used for training, tuning, embeddings, batch processing, or inference | Quotas, placement, scheduling, isolation, utilization, chargeback | Scarce capacity is stranded, oversubscribed, or allocated without priority |
| Model shaping and validation | Training, adaptation, evaluation, red teaming, performance testing, and approval | Reproducibility, test suites, security review, risk thresholds, human approval | A technically deployable model is mistaken for an acceptable service |
| Model registry | Approved models and associated production artifacts | Versioning, signatures, lineage, promotion status, access, retention | Teams deploy unknown, unapproved, or irreproducible artifacts |
| Inference engines | Runtime services that expose AI capabilities to applications and workflows | Identity, segmentation, scaling, rate limits, resilience, rollback | Model access becomes uncontrolled or service behavior becomes unpredictable |
| Observability hub | Infrastructure, runtime, model, security, cost, and outcome telemetry | Correlation, alert ownership, retention, SLOs, evidence, incident workflows | Teams see infrastructure health but miss quality, policy, or business failures |
| Security chambers | Trust boundaries between lifecycle stages and administrative domains | Microsegmentation, least privilege, workload identity, egress control, audit | Compromise or misuse moves laterally across the entire AI lifecycle |
| VCF foundation | Standardized compute, storage, networking, security, automation, and operations | Lifecycle management, configuration standards, capacity, resilience, tenancy | Every AI project rebuilds infrastructure and operational practices independently |
This translation separates platform capabilities from lifecycle outcomes. The infrastructure team can provide a secure registry service, for example, but the model-governance process must still define what qualifies an artifact for the production stage.
Why VMware Cloud Foundation Matters Beneath the AI Lifecycle
The value of VMware Cloud Foundation is not that it magically turns infrastructure into an AI factory. Its value is that it can reduce infrastructure variation beneath the AI lifecycle and give platform teams a consistent place to implement isolation, automation, lifecycle management, and operational visibility.
vSphere Provides the Compute and Workload Foundation
vSphere supplies the virtualization and workload foundation on which management services, data services, model tooling, registries, application components, and AI runtimes can operate. It also gives the platform team a familiar operational boundary for capacity, placement, availability, and lifecycle management.
AI workloads, however, change traditional capacity assumptions. High consolidation ratios may conflict with latency, memory bandwidth, accelerator locality, or predictable inference performance. Platform teams need workload classes that distinguish general compute, data preparation, interactive development, batch training, shared inference, and latency-sensitive production inference.
A capacity plan should therefore model more than total accelerator count. It should include host placement, memory, storage throughput, network behavior, failure domains, maintenance impact, queue depth, utilization targets, and the service consequences of losing a host or accelerator.
vSAN Provides Policy-Driven Storage, Not Automatic Data Architecture
AI services can generate several artifact classes with different performance, durability, replication, retention, and recovery requirements:
- Raw and curated datasets
- Checkpoints and training outputs
- Model binaries and runtime images
- Embedding stores and retrieval indexes
- Prompt and policy versions
- Evaluation evidence
- Logs, traces, and audit records
vSAN can provide policy-driven storage beneath these workloads, but it does not remove the need to classify them. A short-lived training checkpoint should not automatically receive the same retention and recovery treatment as an approved production model or an audit record.
Storage design should identify which artifacts are authoritative, which can be rebuilt, which contain sensitive data, which require immutable retention, and which must be recoverable within a defined service objective.
NSX Turns Security Boundaries into Enforceable Paths
The security chambers in the image are one of its most important ideas. An AI foundry should not be one large trusted network where ingestion systems, training environments, registries, inference services, administrative tools, and business applications communicate broadly.
NSX provides a mechanism for translating lifecycle boundaries into enforceable east-west and north-south policy. The objective is not simply to create segments. It is to describe allowed communication paths precisely:
- Ingestion services may read approved source systems and write curated data.
- Training environments may read curated datasets and write candidate artifacts.
- Validation systems may read candidates and write evaluation evidence.
- Registries may accept approved promotions but should not be directly exposed to general business users.
- Inference services may load approved artifacts and reach only authorized application or data dependencies.
- Management services should remain separate from workload consumption paths.
Effective policy also depends on identity, naming, ownership, and lifecycle integration. A firewall rule that identifies only IP addresses will age poorly in an automated platform. Security intent should follow workload classes, service identities, namespaces, tags, or other stable attributes.
VCF Automation Creates Paved Roads for AI Consumption
A foundry needs repeatable service requests, not ticket-driven assembly. VCF Automation can serve as the entry point for controlled deployment patterns that package infrastructure, networking, storage, policy, ownership metadata, and operational requirements.
A useful AI service catalog might expose requests such as:
- Isolated data-science workspace
- Time-bound accelerator development environment
- Shared embedding-generation service
- Approved model evaluation environment
- Production inference service
- Retrieval-augmented generation application foundation
- Model registry project
- Controlled AI agent execution environment
The catalog should not ask every consumer to design the underlying topology. It should expose the choices that legitimately vary, such as data classification, service tier, capacity class, expected duration, owning team, external dependencies, and approval requirements.
Self-service is valuable only when it eliminates unnecessary variation. Self-service that allows each team to invent its own identity model, network exposure, observability, secrets handling, and recovery design is self-assembly, not a platform.
VCF Operations Supports the Infrastructure Feedback Loop
VCF Operations can contribute infrastructure health, capacity, performance, configuration, and lifecycle signals. Those signals are necessary, but AI service observability requires more than infrastructure telemetry.
A complete observability model should correlate four layers:
- Platform signals: Host health, accelerator state, storage latency, network behavior, capacity, configuration, and lifecycle events.
- Runtime signals: Pod or virtual-machine health, queue depth, request latency, throughput, retries, failures, and scaling.
- Model signals: Evaluation results, response quality, drift, grounding, refusal behavior, safety events, and version distribution.
- Business signals: Adoption, task completion, human override, cost per useful outcome, service value, and unintended impact.
VCF Operations can support the lower layers and provide context for the others. It should not be treated as a substitute for model, application, security, or business observability. The foundry becomes operationally useful when those signal sets can be correlated during incidents, capacity planning, release decisions, and governance reviews.
Security Chambers Should Match Lifecycle Authority
Security boundaries should follow what each stage is authorized to read, change, approve, and publish. The following model is intentionally directional. A real implementation may include additional zones for development, shared services, external access, backup, recovery, and administrative tooling.
This structure prevents a common failure: allowing development convenience to become production authority. A training workspace should not be able to publish directly into production simply because its user created the model. Validation evidence and promotion authority should be independent controls.
The same principle applies to AI agents. An agent that can call an inference service does not automatically need access to the model registry, training data, platform-management interfaces, or unrestricted external systems. Non-human identities require scoped permissions, credential rotation, auditability, rate controls, and explicit tool authorization.
Governance Must Travel with the Artifact
Governance is often presented as a review meeting that occurs before deployment. That model does not scale because the service changes after the meeting. Models are updated, prompts change, retrieval indexes are rebuilt, dependencies move, policies evolve, and new tools are connected.
Governance information must therefore travel with the production artifact. The deployment record should answer:
- What business use case is this service approved to support?
- Which model, prompt, retrieval index, tool, runtime, and policy versions are active?
- Which data classes and sources are permitted?
- Which evaluation evidence supported promotion?
- Who approved the release?
- Which identities may call the service?
- Which systems may the service reach?
- Which telemetry and evidence must be retained?
- What conditions require human review, rollback, restriction, or retirement?
This does not mean storing every control in one registry product. It means maintaining traceable relationships between deployment configuration, model artifacts, data lineage, evaluation results, approvals, identity policies, network policies, and observability records.
The NIST AI Risk Management Framework Generative AI Profile reinforces the need to manage generative AI risks across design, development, use, and evaluation rather than treating risk review as a final deployment step. The operational implication is clear: risk controls must be represented in the lifecycle and produce evidence that can be reviewed later.
An Illustrative AI Service Policy Contract
A platform team needs a machine-readable contract that connects business ownership, data restrictions, model approval, runtime placement, network policy, identity, and observability. The following YAML is illustrative. It is not a native VMware Cloud Foundation API schema.
The contract could be used by a platform orchestration layer to generate or validate requests across VCF Automation, NSX, identity services, model registries, evaluation tooling, and observability systems.
apiVersion: platform.example/v1
kind: AIServicePolicy
metadata:
name: finance-rag-production
spec:
business:
owner: finance-analytics
useCase: internal-policy-assistant
reviewDate: 2026-10-01
data:
classification: restricted
approvedSources:
- finance-policy-repository
- approved-procedure-library
retentionDays: 90
model:
allowedFamilies:
- approved-enterprise-llm
registryStage: production
evaluationGate:
minimumPassRate: 0.95
requireSecurityReview: true
requireBusinessOwnerApproval: true
runtime:
namespace: finance-ai-prod
acceleratorClass: shared-inference
minReplicas: 2
maxReplicas: 6
network:
ingress: private
egress: deny-by-default
permittedDestinations:
- internal-knowledge-api
- enterprise-identity-service
identity:
workloadIdentityRequired: true
humanApprovalRequiredFor:
- model-promotion
- external-tool-execution
observability:
promptLogging: redacted
retainAuditDays: 90
rollbackOn:
- policy-violation
- quality-regression
- sustained-slo-breach
The values that readers would change include the business owner, use case, review date, approved data sources, evaluation threshold, runtime class, scaling range, permitted destinations, approval gates, and retention periods.
Successful execution would not merely create a YAML file. It would result in enforceable platform state:
- The deployment is placed in the correct tenancy boundary.
- Only approved artifacts can enter the production stage.
- NSX policy limits ingress and egress.
- Workload identity is issued and scoped.
- Evaluation and approval evidence is attached to the release.
- Required telemetry and audit retention are enabled.
- Rollback conditions are connected to an incident or release workflow.
The most common failure with policy-as-code is treating the file as documentation. A policy contract has operational value only when admission, deployment, network, identity, registry, and observability systems validate or enforce it.
The Operating Model Is the Real Architecture
The image makes the platform appear vertically integrated, but most enterprises divide responsibility across several teams. The architecture will fail if those ownership boundaries remain implicit.
| Capability | Accountable owner | Operational responsibility |
|---|---|---|
| VCF platform and lifecycle | Private-cloud platform owner | Capacity, upgrades, compatibility, resilience, certificates, backup, and support |
| Accelerated compute | Platform and infrastructure owners | Accelerator profiles, scheduling, placement, utilization, maintenance, and capacity planning |
| Network and security | Network and security owners | Segmentation, ingress, egress, inspection, administrative access, and policy evidence |
| Data sources and classification | Data owners and stewards | Approved use, quality, lineage, retention, residency, and access |
| Model lifecycle | AI or machine-learning platform owner | Evaluation, registry stages, reproducibility, promotion, rollback, and retirement |
| AI application | Application or product owner | User experience, business logic, service integration, testing, and outcome ownership |
| Risk and governance | Risk, legal, security, and business owners | Control requirements, exceptions, approvals, evidence, and review cadence |
| Service operations | Joint platform and application operations | SLOs, alerts, incidents, capacity, release coordination, and recovery |
A RACI matrix alone will not make the model work. Teams also need decision rights. The organization must define who can approve a model, who can authorize new data, who can permit external tool execution, who can accept residual risk, and who can stop the service.
AI platform ownership should not become a reason for every other team to transfer accountability. The platform team supplies repeatable controls and paved roads. Data owners remain accountable for data. Application owners remain accountable for the user-facing service. Security remains accountable for policy. Business owners remain accountable for the use case and its consequences.
A Practical Implementation Path
Building an enterprise AI foundry should be a staged operating-model program, not a hardware installation followed by an open-ended search for use cases.
Start with Service Definitions, Not Infrastructure Inventory
Select a small set of representative services and define their operating requirements. A useful first portfolio may include a development workspace, a document-grounded assistant, a batch embedding service, and a production inference endpoint.
For each service, document:
- Business owner and intended outcome
- Data sources and classifications
- Availability and performance expectations
- Accelerator and storage requirements
- Network dependencies
- Identity model
- Evaluation and approval requirements
- Evidence retention
- Cost-allocation model
- Rollback and recovery expectations
These service definitions expose which platform capabilities are actually necessary.
Baseline the VCF and Accelerator Foundation
Validate the VCF 9.1 design, lifecycle baseline, hardware compatibility, capacity, network architecture, storage policies, management dependencies, certificate design, backup, recovery, and operational ownership.
Accelerator validation should include scheduling and placement behavior, maintenance impact, expected concurrency, failure handling, and workload-specific performance. A successful driver installation is not sufficient proof that the production service can meet its objectives.
Create Lifecycle Zones and Tenancy Boundaries
Map ingestion, development, training, validation, registry, inference, and management functions to explicit workload and trust boundaries. Decide which stages can share clusters or infrastructure and which require stronger isolation.
Apply identity and network controls together. A segmented network with broadly privileged service accounts is not least privilege. A strong identity model with unrestricted egress is also incomplete.
Build Golden Paths Through VCF Automation
Encode approved service patterns as catalog or API-driven requests. Each pattern should establish placement, storage, network policy, ownership metadata, identity integration, telemetry, expiration, and support expectations.
Begin with constrained choices. Add flexibility only after a recurring requirement is understood. Platform products become difficult to operate when every exceptional request becomes a permanent catalog option.
Establish Evaluation and Promotion Gates
Define what each service class must prove before moving from development to validation and from validation to production. Gates may include functional evaluation, security review, prompt-injection testing, privacy review, performance, resilience, cost, human approval, and business-owner acceptance.
Store evidence with the release. A passing score without the tested version, data set, prompt, configuration, and environment is not reproducible evidence.
Connect Infrastructure and AI Observability
Correlate VCF health and capacity with application latency, runtime behavior, model version, evaluation trends, policy events, and business outcomes. Assign ownership for each alert class and define escalation paths across platform, application, security, data, and AI teams.
Avoid alerting on every model metric without a response plan. A signal becomes operationally useful only when someone knows what condition it represents and what action to take.
Scale Only After the Control Loop Works
A pilot is not complete merely because users can send prompts. The pilot should demonstrate that the organization can deploy, observe, restrict, update, recover, and retire the service.
Scale after the feedback loop has been exercised:
The foundry is mature when this loop is repeatable across services without requiring every project to invent new platform, security, and governance processes.
Where the Foundry Metaphor Breaks
The foundry metaphor creates useful discipline, but it can also imply a linear and fully contained process. Enterprise AI is rarely that simple.
Models may come from external providers. Data may remain in operational systems. Retrieval services may cross platform boundaries. Agents may call business APIs. Human decisions may interrupt automated workflows. A single application may use several models, policies, and data services. Some evaluation happens before deployment, while other evaluation depends on production behavior.
Several practical caveats follow:
- The VCF foundation is not the complete governance system. It must integrate with data, identity, model, security, risk, and application controls.
- Private placement does not automatically make a service appropriate or trustworthy. Data handling, model behavior, authorization, and outcome risk still require assessment.
- NSX cannot determine whether an answer is accurate. It can restrict communication, reduce lateral movement, and enforce access paths.
- VCF Operations does not replace model and application telemetry. Infrastructure health must be correlated with higher-level behavior.
- Automation accelerates unsafe patterns as efficiently as safe ones. Catalog items and APIs require policy, review, versioning, and lifecycle ownership.
- A model registry is not a complete release record. Prompt versions, retrieval indexes, runtime images, policies, evaluation evidence, and approvals also matter.
- Accelerator availability does not prove economic efficiency. Utilization, scheduling, latency, power, support, licensing, and operational labor affect the total cost.
- Resilience extends beyond the VCF cluster. Identity, DNS, certificates, registries, data stores, secrets, external APIs, and recovery procedures are part of the service.
These limitations do not weaken the foundry model. They prevent the metaphor from becoming a vendor-shaped simplification.
Decision Criteria for a VCF-Based AI Foundry
A VCF-based AI foundry is a strong fit when the organization needs private-cloud control, has significant data gravity, requires predictable placement or latency, wants common operations for traditional and AI workloads, and is prepared to build the lifecycle and governance capabilities around the infrastructure.
It may be a weaker fit when the organization has only intermittent AI demand, lacks platform and AI operations skills, depends primarily on rapidly changing hosted model services, or cannot assign ownership across data, security, infrastructure, models, applications, and risk.
The decision should consider:
- Data residency, sovereignty, privacy, and gravity
- Latency and application-integration requirements
- Consistency and duration of training and inference demand
- Accelerator utilization and capacity-pooling opportunities
- Existing VCF skills, support processes, and operational maturity
- Ability to own upgrades, security, observability, recovery, and incident response
- Need for infrastructure control versus speed of consuming managed services
- Total operating cost rather than hardware acquisition cost alone
- Portability and exit requirements for models, data, runtime components, and policies
- Governance obligations for high-impact use cases
Hybrid operation is often the practical answer. The objective should not be to force every AI workload into one location. The objective should be to apply one decision framework and one evidence model across private, hosted, and hybrid services.
Conclusion
The most valuable idea in the image is not the underground machinery or the visible accelerator layer. It is the separation between the AI outcomes the enterprise consumes and the production system required to create and sustain them.
VMware Cloud Foundation 9.1 can provide a strong substrate for that system. vSphere supplies the workload foundation, vSAN supports policy-driven storage, NSX enforces communication boundaries, VCF Automation creates repeatable service paths, and VCF Operations contributes infrastructure visibility and lifecycle context. Private AI integrations can extend that foundation into accelerated AI workloads and supporting model services.
The foundry still requires an operating model. Data must have owners. Models and related artifacts must be evaluated and versioned. Promotion needs evidence and authority. Network and identity policies must follow lifecycle stages. Observability must connect infrastructure health to model behavior and business outcomes. Rollback and retirement must be designed before they are needed.
The enterprise AI foundry is therefore not an AI laboratory with better hardware. It is a repeatable production system that turns approved data, models, policies, infrastructure, and human decisions into AI services the organization can operate responsibly.
External References
- Broadcom TechDocs: VMware Cloud Foundation 9.1
Canonical URL: https://techdocs.broadcom.com/us/en/vmware-cis/vcf/vcf-9-0-and-later/9-1.html - Broadcom TechDocs: VMware Cloud Foundation 9.1 Release Notes
Canonical URL: https://techdocs.broadcom.com/us/en/vmware-cis/vcf/vcf-9-0-and-later/9-1/release-notes/vmware-cloud-foundation-9-1-0-0-release-notes.html - Broadcom TechDocs: VMware Private AI Foundation with NVIDIA 9.1
Canonical URL: https://techdocs.broadcom.com/us/en/vmware-cis/private-ai/foundation-with-nvidia/9-1.html - Broadcom TechDocs: VCF Automation Overview
Canonical URL: https://techdocs.broadcom.com/us/en/vmware-cis/vcf/vcf-9-0-and-later/9-1/overview-of-vmware-cloud-foundation-9/what-is-vmware-cloud-foundation-and-vmware-vsphere-foundation/vcf-automation-overview.html - Broadcom TechDocs: NSX Overview
Canonical URL: https://techdocs.broadcom.com/us/en/vmware-cis/vcf/vcf-9-0-and-later/9-1/overview-of-vmware-cloud-foundation-9/what-is-vmware-cloud-foundation-and-vmware-vsphere-foundation/nsx-overview.html - Broadcom TechDocs: Architectural Options in VMware Cloud Foundation
Canonical URL: https://techdocs.broadcom.com/us/en/vmware-cis/vcf/vcf-9-0-and-later/9-1/design/vmware-cloud-foundation-concepts.html - NVIDIA: NVIDIA Enterprise AI Factory Design Guide White Paper
Canonical URL: https://docs.nvidia.com/ai-enterprise/planning-resource/ai-factory-white-paper/latest/index.html - National Institute of Standards and Technology: Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile
Canonical URL: https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence
TL;DR A private AI model factory is not simply a cluster of GPU servers. It is a governed operating model that turns...