Broadcom made VMware Private AI Services a standard part of VMware Cloud Foundation (VCF) 9.0, bringing model and agent development, retrieval, and GPU monitoring into its private-cloud platform. In August 2026, it expanded that strategy with VMware Private AI Cloud, a broader production offering for running and governing AI inference alongside traditional workloads. The aim is to let organizations operate AI in their own environments; the announcements do not establish that every deployment automatically keeps all data on premises or meets every compliance requirement.
What Broadcom added to VMware Cloud Foundation
On August 26, 2025, Broadcom announced that VMware Private AI Services would be included in the VCF 9.0 subscription rather than sold separately. Broadcom said organizations could run AI and non-AI workloads on the same platform without an additional purchase for those services. VCF itself is a subscription, and Broadcom says customers buy it directly from Broadcom or an authorized Broadcom partner.
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The included services cover several stages of working with AI models and applications:
| Service | What Broadcom says it provides |
|---|---|
| GPU Monitoring | Visibility into GPU use and performance. |
| Model Store | A place to manage and share models. |
| Model Runtime | Platform services for running models. |
| Agent Builder | Tools for building agentic applications. |
| Vector Database | Vector storage used in AI application workflows. |
| Data Indexing/Retrieval | Services for indexing information and retrieving relevant data for AI applications. |
Broadcom’s August 31, 2026 announcement of VMware Private AI Cloud describes a wider production path that brings inference workloads, agentic applications, and traditional workloads together. It extends the private-AI positioning beyond the 2025 VCF 9.0 services announcement; the two announcements should not be mistaken for a single product name or release date.
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Is VCF 9.0 an AI platform?
VCF 9.0 is best understood as a private-cloud platform with integrated AI services, rather than as an AI model or a standalone public-cloud AI service. The offering combines infrastructure and platform capabilities for running models and AI applications with an organization’s other workloads. Broadcom presents model operations, retrieval, observability, and agent building as parts of that platform.
That distinction matters when evaluating it: integrating AI services into private-cloud infrastructure may help organizations use existing operational practices, but it does not by itself specify how a particular application should be designed, how its data should be governed, or whether it will satisfy a given regulatory obligation. Those outcomes depend on deployment choices and the organization’s controls.
Which GPUs and models does VMware Private AI support?
Accelerator options
Broadcom describes support paths for NVIDIA and AMD accelerators, including mixed CPU/GPU infrastructure, and says VCF supports NVIDIA Blackwell. A VMware product blog published August 26, 2025 quoted an NVIDIA specification of up to eight NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs per server. That is a per-server hardware specification, not a claim that every VCF deployment uses that configuration or achieves a particular level of performance.
Model choice
In its August 31, 2026 announcement, Broadcom said more than 150 open-source and commercial AI models were available on VCF. It named Nemotron 3, Gemma 4, cotomi, Qwen 3.7-Max, and GLM 5.2 among validated models. Broadcom describes these as validated for VCF; the announcement does not establish that every model version, hardware combination, or use case has identical support or performance. Check Broadcom’s current compatibility information before choosing a specific deployment.
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Can enterprises run inference without moving data to a public cloud?
That is the central use case Broadcom promotes: operating models and AI applications in an enterprise-controlled environment so organizations can retain control over where data and models run. VMware Private AI Cloud is described as a way to build, run, and govern inference workloads and agentic applications alongside other workloads.
Private infrastructure can support data-sovereignty and compliance goals, but the product announcement is not a blanket guarantee of either. Data movement also depends on the application, integrations, model sources, telemetry, backups, and the way the environment is configured. Organizations with residency or regulatory requirements should map those flows and verify the controls in their own architecture rather than infer compliance from the “private AI” label.
How Broadcom says it addresses GPU and token costs
Broadcom identifies hardware capital expense, operational complexity, and token economics as challenges for production AI. Its August 31, 2026 announcement names several capabilities intended to help manage them:
- NVMe memory tiering and cluster-wide storage deduplication are presented as infrastructure-efficiency measures.
- Enhanced GPU and vGPU tracking is intended to improve visibility into accelerator use.
- Token monitoring is aimed at tracking a key usage measure for AI workloads.
- Multi-tenant model sharing is intended to let teams share models across tenants rather than treat every deployment as an isolated copy.
These are cost-management mechanisms, not evidence of a guaranteed reduction in total cost. The announcements provide no deployment-specific price, savings estimate, or independent cost comparison. A buyer still needs to account for VCF subscription terms, accelerator capacity, storage, operations, model usage, and any separately purchased software.
What Broadcom’s adoption figures do—and do not—show
Broadcom said in 2025 that 100 million VCF cores were licensed and that nine of the top 10 Fortune 500 companies had committed to VCF. Those are company-reported figures about VCF adoption, not measurements of Private AI Services usage.
Broadcom’s 2026 announcement also cited its Private Cloud Outlook 2026, reporting that 56% of enterprises were already running or planning production AI inference on private cloud. This is a survey finding as reported by Broadcom; it does not mean that 56% had deployed VMware Private AI Cloud.
What to verify before choosing it
VCF’s integrated services may be relevant to organizations already considering or using VMware private-cloud infrastructure, especially when they want to run AI near enterprise data. A product announcement alone does not establish the best fit or total cost for a particular environment. Validate these points with Broadcom or an authorized partner:
- The VCF subscription and support terms that apply to your organization, region, and deployment.
- Current compatibility for the exact GPU, server, model version, and application you intend to run.
- How identity, tenant isolation, data retrieval, logging, and model governance will work in your architecture.
- What GPU, vGPU, storage, and token monitoring can report, and how those reports fit your cost controls.
- Whether any additional vendor software or services are needed. Broadcom’s 2025 VMware blog says NVIDIA AI Enterprise is purchased directly from NVIDIA for relevant vGPU and NVIDIA NIM deployments.
Broadcom has also reported that independent MLPerf Inference v5.1 testing found performance “on par with bare metal.” That is Broadcom’s characterization of the cited benchmark testing, not an independent performance result established here; actual results depend on the tested configuration and workload.
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