{"id":5726,"date":"2026-09-22T10:35:02","date_gmt":"2026-09-22T10:35:02","guid":{"rendered":"https:\/\/cloudobjectivity.co.uk\/?p=5726"},"modified":"2026-10-11T12:29:41","modified_gmt":"2026-10-11T13:29:41","slug":"optimizing-ai-deployments-with-vmware-cloud-foundation-part-1","status":"publish","type":"post","link":"http:\/\/cloudobjectivity.co.uk\/index.php\/2026\/09\/22\/optimizing-ai-deployments-with-vmware-cloud-foundation-part-1\/","title":{"rendered":"Optimizing AI Deployments with VMware Cloud Foundation (Part 1)"},"content":{"rendered":"\t\t
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Published: September 22, 2026<\/p>\n\n

Executive Overview<\/h5>\n\n

In the rapidly evolving landscape of enterprise artificial intelligence, a critical challenge has emerged: bridging the gap between the theoretical potential of AI models and the practical, economic realities of deploying them at scale. The physical infrastructure required for advanced AI\u2014specifically, high-end compute and specialized accelerators like GPUs\u2014is experiencing unprecedented demand, leading to significant capital expenditure pressures and supply chain constraints. For the Enterprise Chief Information Officer (CIO), the mandate is no longer simply acquiring AI capabilities; it is maximizing the utilization and efficiency of every expensive component within the data center.<\/p>\n\n

The core issue facing many organizations is resource stranding and underutilization. While “hero runs” and massive training jobs can fully saturate a server’s resources, the reality of many real-world AI inference workloads is that they are lightweight, characterized by lower request rates or smaller batch sizes. Provisioning dedicated, bare-metal hardware for these types of workloads results in massive inefficiencies, driving up operational costs and stalling the broader democratization of AI within the enterprise.<\/p>\n\n

This analysis examines the VMware Cloud Foundation (VCF) approach to addressing this challenge, focusing on its recent NVIDIA Hypervisor-Certification. With vSphere 9.1 achieving this status, VMware asserts that VCF can deliver near bare-metal performance for AI workloads while providing the essential virtualization features needed for modern data centers. The focus is on multi-tenant environments\u2014where departments, projects, or external customers share the same underlying hardware. By leveraging VCF’s capabilities for isolation, resource optimization, and self-service, enterprises can theoretically transform static, underutilized AI hardware into an operationally elastic, enterprise-grade, private AI cloud.<\/p>\n\n

Through extensive performance testing conducted by the Broadcom Performance team, this first part of a broader series explores the architectural flexibility VCF provides. Specifically, it analyzes the performance implications of different multi-tenant deployment models\u2014comparing strict VM-based isolation against container-based consolidation\u2014and evaluates the efficacy of GPU allocation methods such as direct passthrough and NVIDIA vGPU in driving up overall system utilization and throughput.<\/p>\n\n

Features<\/h5>\n\n

VMware Cloud Foundation introduces several key technical features designed to operationalize AI within multi-tenant, enterprise environments. These features are focused on balancing the need for strict workload isolation with the economic necessity of high hardware utilization.<\/p>\n\n

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    NVIDIA Hypervisor-Certification (vSphere 9.1+):<\/strong> A foundational feature of the current VCF architecture is its certification as an NVIDIA-Certified Hypervisor, starting with vSphere 9.1. This certification is critical as it validates the hypervisor’s ability to run AI workloads with performance levels approaching that of a bare-metal deployment. This feature provides the technical underpinning required to confidently virtualize high-value, performance-sensitive AI applications without incurring unacceptable overhead.<\/p>\n<\/li>\n\n

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    Flexible Tenancy Deployment Models:<\/strong> VCF allows IT administrators to manage multi-tenant environments through distinct deployment models, catering to varying security and operational requirements. These models can be managed either manually or orchestrated via VCF Automation. This flexibility is a core feature, allowing organizations to tailor the infrastructure to the specific needs of different departments or projects sharing the same physical hardware.<\/p>\n<\/li>\n\n

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    VM-Based Workload Isolation:<\/strong> For environments requiring strict security boundaries, VCF supports a deployment model where each tenant’s workloads are encapsulated within separate Virtual Machines (VMs). This feature ensures robust isolation between different users or applications. Within this model, GPUs can be allocated to the individual VMs using either direct GPU passthrough or NVIDIA vGPU technology. This configuration is fully supported by VCF Automation.<\/p>\n<\/li>\n\n

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    Container-Based Workload Consolidation:<\/strong> As an alternative for environments where workload density and simplified management take precedence over strict VM-level security, VCF allows for multiple tenants’ workloads to be consolidated within the same VM, isolated via containers. GPUs are still assigned to this shared VM via passthrough or vGPU. It is important to note that this specific consolidation feature is currently only supported when managing tenancy manually, as VCF Automation enforces dedicated VMs per tenant.<\/p>\n<\/li>\n\n

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    Versatile GPU Allocation (Passthrough, vGPU, MIG vGPU):<\/strong> A critical feature for maximizing hardware utilization is VCF’s support for various GPU allocation methods. IT administrators can utilize direct GPU passthrough for workloads requiring exclusive access to a physical GPU. Alternatively, they can employ NVIDIA vGPU (including Multi-Instance GPU or MIG vGPU capabilities) to allocate full or fractional (partial) GPU resources to VMs. This enables the sharing of expensive GPU resources across multiple tenant VMs, dramatically improving overall utilization rates compared to dedicated hardware assignments.<\/p>\n<\/li>\n<\/ul>\n\n

    Benefits<\/h5>\n\n

    The implementation of these features within VMware Cloud Foundation offers significant strategic and economic benefits for enterprises attempting to scale their AI operations.<\/p>\n\n

    The primary benefit is the Maximization of Hardware Utilization and ROI<\/strong>. As demonstrated in the Broadcom Performance team’s experiments, many real-world AI workloads (such as the Llama3.1 8B inference workload tested) significantly underutilize modern hardware when deployed in isolation. The tests showed GPU Streaming Multiprocessor utilization languishing at 36%-38% for a single tenant. By leveraging VCF to consolidate multiple concurrent workloads onto the same server, utilization increased to 71%. This capability to drive up utilization rates directly translates to a better Return on Investment (ROI) on expensive AI infrastructure, allowing organizations to do more with less hardware.<\/p>\n\n

    Secondly, VCF delivers Massive Throughput Scaling<\/strong>. The ability to increase hardware utilization is only valuable if it translates to increased output. The Broadcom tests confirmed that by increasing the number of concurrent tenants on a single server from one to four, the total inference throughput scaled almost linearly, jumping from approximately 4,500 tokens per second to nearly 18,000 tokens per second. This demonstrates that VCF’s virtualization layer does not create a bottleneck when scaling workloads to maximize server capacity.<\/p>\n\n

    Thirdly, the platform provides Architectural Flexibility Without Performance Penalties<\/strong>. A critical finding of the analysis is the comparable performance between different isolation methods. The experiments showed “almost no difference in throughput” whether the workloads were strictly isolated in separate VMs or consolidated within containers inside a shared VM. This is a significant benefit, as it allows architects to choose the deployment model that best fits their security and operational requirements without sacrificing performance. They can enforce strict VM-based isolation for sensitive projects or opt for denser container consolidation for internal development, knowing the underlying infrastructure will deliver consistent throughput.<\/p>\n\n

    Finally, VCF enables Operational Elasticity and Self-Service<\/strong>. By transforming physical AI hardware into a flexible, software-defined resource pool, VCF allows IT to provision resources dynamically. Whether using dedicated servers for high-demand “hero runs” or consolidating lightweight inference workloads, the infrastructure can adapt to changing business needs. When combined with VCF Automation, this enables self-service capabilities for data science teams, accelerating the deployment of AI projects while maintaining centralized IT control and resource optimization.<\/p>\n\n

    Use cases<\/h5>\n\n

    The flexible architecture of VCF for AI workloads addresses several common deployment scenarios within the modern enterprise.<\/p>\n\n