| Deployment & Infrastructure | What kind of infrastructure is required to run VMware Private AI on-premises? | VMware vSphere / VCF 9 clusters with NVIDIA GPUs (Blackwell GPUs recommended for high-performance AI workloads). |
| Deployment & Infrastructure | Can it run on existing VMware vSphere clusters or do we need new hardware? | Yes, it integrates with existing vSphere clusters; additional GPUs may be needed for large AI workloads. |
| Deployment & Infrastructure | How does it integrate with our current VMware Cloud Foundation setup? | Fully integrated with VCF 9, allowing seamless deployment of Private AI services on existing infrastructure. |
| Deployment & Infrastructure | Does it support hybrid deployments with public cloud AI services? | Primarily on-premises, but hybrid integration with public cloud is possible if required. |
| AI Workload & Performance | How much faster is AI training and inference compared to vSphere 8 or previous VCF versions? | Significantly faster due to DirectPath GPU access and NVIDIA HGX integration. |
| AI Workload & Performance | Can we run large-scale GenAI models on-premises efficiently? | Yes, scalable AI workloads are supported and optimized for GPU usage and memory throughput. |
| AI Workload & Performance | How does GPU allocation work across multiple AI workloads? | GPU allocation and scheduling isolate workloads to ensure performance without affecting other VMs. |
| AI Workload & Performance | What is the impact on other workloads running on the same cluster? | Minimal; non-AI workloads continue to operate normally thanks to GPU isolation and resource management. |
| Security & Compliance | How is sensitive data protected in on-prem AI deployments? | Fully on-premises deployment ensures data remains private; supports air-gapped setups. |
| Security & Compliance | Can we run this in an air-gapped environment? | Yes, Private AI supports air-gapped deployments for maximum security. |
| Security & Compliance | How does VMware ensure compliance with GDPR, HIPAA, or other regulations? | On-prem deployments give full control over data; platform follows VMware security best practices and compliance guidelines. |
| Management & Automation | How are AI models deployed, patched, and updated? | Automated via Model Store and Model Runtime for lifecycle management, updates, and rollbacks. |
| Management & Automation | Can we automate scaling of AI workloads? | Yes, horizontal and vertical scaling is supported for dynamic workload management. |
| Management & Automation | How do we monitor GPU usage, AI job performance, and system health? | Through VMware tools like vRealize Operations, NSX dashboards, and built-in AI service monitoring. |
| Cost & TCO | How much cost savings can we expect compared to public cloud AI services? | Lower TCO due to reduced public cloud dependency, optimized GPU usage, and reduced operational overhead. |
| Cost & TCO | Does VMware provide licensing for GPUs, or do we need to buy separately? | GPUs are purchased separately; VMware provides software licensing for Private AI services. |
| Cost & TCO | What is the ROI for moving GenAI workloads on-premises? | Faster AI deployment, reduced cloud spend, improved resource utilization, and enhanced control contribute to high ROI. |
| Use Cases & Flexibility | Can we deploy predictive AI for infrastructure management? | Yes, fully supported, including AI-driven monitoring, predictive analytics, and automated remediation. |
| Use Cases & Flexibility | Can we run agentic AI solutions for self-healing infrastructure? | Yes, agentic AI workloads can autonomously take corrective actions for IT systems. |
| Use Cases & Flexibility | Does it support multiple AI frameworks like TensorFlow, PyTorch, or custom models? | Yes, TensorFlow, PyTorch, and custom AI models are fully supported. |
| Use Cases & Flexibility | How does it compare to running AI workloads in AWS, Azure, or GCP? | Comparable performance with GPUs, but fully on-premises for better control, privacy, and cost efficiency. |
| Migration & Upgrade | What’s involved in migrating from vSphere 8 / VCF 5.x to VCF 9 with Private AI? | Upgrade VCF to version 9, enable GPU passthrough, and activate Private AI services. |
| Migration & Upgrade | Can existing AI workloads (e.g., chatbots on public cloud) be brought on-prem? | Yes, workloads can be migrated with proper planning and integration into Private AI services. |
| Migration & Upgrade | How long does it take to enable AI services after upgrading to VCF 9? | Typically minutes to a few hours, depending on cluster size and GPU availability. |
| Support & Ecosystem | What kind of NVIDIA GPUs are supported? | Blackwell GPUs with NVSwitch are recommended; other supported NVIDIA GPUs can be used for smaller deployments. |
| Support & Ecosystem | Are there pre-built AI blueprints or templates? | Yes, VMware provides pre-built AI microservices and blueprints for faster deployment. |
| Support & Ecosystem | How does VMware support troubleshooting and optimization for AI workloads? | Through VMware support, monitoring tools, best-practice guides, and NSX dashboards for optimization. |
| Support & Ecosystem | Are there partners or consulting services to help deploy AI use cases faster? | Yes, VMware ecosystem partners and consulting services assist with deployment and architecture planning. |
