Broadcom announced VMware Cloud Foundation (VCF) 9.1, a secure and cost-effective infrastructure platform for production AI workloads.
VCF 9.1 delivers an AI and Kubernetes native private cloud platform with integrated security and mixed compute infrastructure support across AMD, Intel, and NVIDIA. This enables enterprises to deploy inference and agentic AI applications with significantly lower costs, enhanced security, and freedom to choose best-of-breed GPU and CPU hardware.
VMware Cloud Foundation provides a better alternative to public cloud for production workloads through intelligent software that maximizes infrastructure efficiency on existing servers while providing architectural control and regulatory compliance capabilities essential for production AI deployments. VMware Cloud Foundation 9.1 will enable enterprises to deploy production workloads including inference and agentic AI with:
- Up to 40% reduction in server costs through intelligent memory tiering for clusters running a mix of AI and non-AI workloads
- Up to 39% lower storage TCO through enhanced compression and deduplication for AI data pipelines
- Up to 46% reduction in Kubernetes operational costs for running AI workloads at scale
- 4x faster cluster upgrades and 2x increased fleet capacity to rapidly scale AI infrastructure
- These numbers are based on internal Broadcom estimates or test results, subject to change - April 2026
“As more enterprises turn to AI for driving competitive advantage, they face three critical challenges: data and IP privacy concerns, surging infrastructure costs, and their readiness for the world of agentic AI,” said Krish Prasad, senior vice president and general manager, VMware Cloud Foundation Division, Broadcom. “VCF 9.1 is a single unified platform that addresses all three and delivers one of the most advanced infrastructure for Private AI. We enable zero-trust security for AI, reduce costs through intelligent infrastructure optimization and hardware choice, and provide the flexibility to run both agentic workflows and accelerated inferencing on the same platform.”
VCF 9.1 maximizes density for both VM and containerized AI workloads on existing infrastructure while dramatically reducing operational complexity. Through intelligent resource management and automated operations, enterprises can deploy more production workloads on current servers, scale efficiently across distributed environments, and eliminate the need for costly infrastructure expansion during a period of hardware shortage and rising costs. Key capabilities include:
- Intelligent resource optimization that maximizes infrastructure utilization through advanced memory tiering and next-generation storage compression for AI data pipelines, enabling higher AI workload density without performance compromises or expensive hardware refresh.
- Automated fleet operations at scale that deliver doubled management capacity to 5,000 hosts and 4x faster cluster upgrades across distributed and air-gapped environments, eliminating manual patching overhead while supporting rapid AI infrastructure expansion.
- Multi-tenant infrastructure for AI isolation that enables enterprises and service providers to run multiple AI projects and customers on shared infrastructure with strict security boundaries, maximizing utilization of expensive GPU and CPU resources while supporting data sovereignty for sensitive models.
- Open ecosystem integration that delivers multi-accelerator GPU choice across AMD and NVIDIA, support for leading AMD and Intel CPU platforms, and standards-based EVPN and VXLAN interoperability with Arista Universal Cloud Network, demonstrating VCF's commitment to providing the high-performance connectivity and compute flexibility production AI demands.
- High speed networking for AI workloads through VCF support for NVIDIA ConnectX-7 NICs and NVIDIA BlueField-3 with Enhanced DirectPath I/O. With this enhancement high-speed, multi-host AI model training and data transfer, crucial for demanding Gen AI workloads is enabled.
- Virtualized load balancing and security with VMware Avi Load Balancer2 and VMware vDefend2 eliminate hardware appliance requirements for AI inference endpoints and agentic applications, reducing capital expense while providing enterprise-grade resilience and automated lifecycle management.
VCF 9.1 delivers a unified platform that accelerates AI application deployment by running inference workloads, agentic applications, containerized services, and traditional VMs on a single infrastructure layer. This eliminates operational fragmentation and the cost of managing separate stacks while providing the developer velocity and platform governance that production AI requires. Key capabilities include:
- Kubernetes scale and performance for AI that delivers 2.6x increased cluster scale, 70% faster deployments, 75% shorter upgrade windows compared to preview versions1, and seamless scaling that enables zero downtime for production AI services.
- Mixed compute management that efficiently handles both CPU-intensive agentic AI workflows and GPU-accelerated inference on a unified platform, addressing the reality that agentic workloads demand significantly more CPU than GPU capacity for workflow execution and decision orchestration.
- AI observability and governance that provides detailed metrics for time to first token, token throughput, and GPU utilization across multiple accelerator types, enabling enterprises to maximize infrastructure ROI through precise hardware utilization monitoring while centralized policy injection and data sovereignty controls enable AI compliance enforcement and secure model access.
- Live application stack blueprints that capture multi-VM applications as reusable templates for rapid environment deployment, eliminating manual configuration errors and preventing configuration drift across development, test, and production environments while accelerating infrastructure delivery velocity.
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