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VCF 9.1.1 - Simplified vSphere Kubernetes Service (VKS) using VLAN-Backed VPCs without NSX Tunnel Endpoints (TEPs)

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As part of the VMware Cloud Foundation (VCF) 9.1.1 release, consuming NSX Virtual Private Cloud (VPC) has gotten simpler, especially for environments using traditional VLAN-backed networks. In VCF 9.1.0, users already had the ability to configure a VPC using a Distributed Transit Gateway (DTGW) and Distributed VLAN through the NSX Virtual Network Appliance (VNA), but this still required configuring NSX Tunnel Endpoints (TEPs) on each ESX host.

With VCF 9.1.1, the networking workflow has been further simplified with a new VLAN-backed VPC option. While it still uses DTGW and VNA, TEPs are no longer required, removing additional network requirements such as a larger MTU. As a result, environments that previously could not meet the networking requirements to configure a VPC can now deploy and consume services such as vSphere Kubernetes Service (VKS) and VCF Automation (VCFA).

It is also important to understand the different VPC consumption options and their constraints. The following table summarizes the capabilities and limitations of each option.


Note: DTGW with TEP was possible with VCF 9.0.0 but there was no support for VCFA until VCF 9.1.0.

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VCF 9.1.1 - Using VCF Private AI Services (PAIS) Models with AI Assistant for VCF

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For those following along, earlier this week I demonstrated how to configure the new AI Assistant for VCF in VMware Cloud Foundation (VCF) 9.1.1 using Google Gemini as a cloud-based LLM provider, which is a quick way to get started and explore the new capability.

Alongside VCF 9.1.1, we also released VCF Private AI Services (PAIS) 3.0, which enables users to deploy and run their own AI models locally within VCF. In my follow-up articles, I walked through setting up PAIS for an air-gapped environment and then deploying an AI model downloaded from Hugging Face, which PAIS serves through an OpenAI-compatible model endpoint.

In this final blog post, we will bring everything together by connecting AI Assistant for VCF to our PAIS Model Endpoint, allowing the AI Assistant to use an AI model running locally within our VCF environment.

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VCF 9.1.1 - Deploying Your Own AI Models with VCF Private AI Services (PAIS) 3.0

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My recent blog post on the new AI Assistant for VCF in VCF Operations 9.1.1 received a lot of positive feedback from both internal and external readers, especially from those who were not aware that this capability was available and were excited to try it out. The excitement is understandable, as this opens up many interesting possibilities for infrastructure administrators to operate and manage their environments more efficiently.

In that article, I focused on how to enable AI Assistant for VCF and quickly get started using Google Gemini as a cloud-based LLM provider. While that is certainly the fastest way to start exploring the capability, AI Assistant for VCF also supports local AI models deployed using VCF Private AI Services (PAIS) 3.0, enabling customers to take advantage of powerful AI models running securely and more cost-effectively within their own on-premises environment.

In this blog post, we will now focus on deploying and consuming local AI models with PAIS. This builds on another recent article where I walked through the setup of VCF Private AI Services (PAIS) using the Artifact Mirroring Tool (AMT) for air-gapped environments. 

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