A preview of Broadcom’s Private
- Up to 40% reduction in server costs through intelligent memory tiering for clusters running a mix of AI and non-AI workloads1;
- Up to 39% lower storage TCO through enhanced compression and deduplication for AI data pipelines1;
- Up to 46% reduction in Kubernetes operational costs for running AI workloads at scale1;
- 4x faster cluster upgrades and 2x increased fleet capacity to rapidly scale AI infrastructure1.
“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
Efficient Infrastructure at Scale for AI Workloads
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.
High Velocity App Delivery: Modern Workload Platform for AI, Containers, and VMs
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.
Zero-Trust Architecture for AI Data Sovereignty and Governance
VCF 9.1 integrates security at the infrastructure layer to protect AI workloads, proprietary models, and training data from hypervisor to application. By delivering zero-trust segmentation, sovereign recovery, and continuous patching without bolt-on tools, VCF strengthens the security posture essential for production AI deployments that public cloud environments cannot match. Key capabilities include:
- On-premises ransomware recovery that provides isolated recovery environments and integrated validation tools including new CrowdStrike Falcon® Endpoint Security support protect AI models and training data – significant intellectual property – from cross-border movement while avoiding massive bandwidth fees during crisis restoration.
- Continuous compliance enforcement2 that maintains regulatory adherence through centralized monitoring and automated desired state remediation for workloads and VCF stack components, enabling enterprises to demonstrate audit readiness for production AI deployments without manual overhead or separate compliance tools.
- Zero-downtime live patching that supports up to 80% of use cases without host evacuation or maintenance windows, eliminating disruption to production AI inference services and agentic applications that require continuous availability for service level agreements1.
- Zero-trust lateral security2 that extends distributed IDS/IPS protection to Kubernetes AI workloads for the first time, delivering 9 Tbps threat inspection performance for distributed inference and 5x increased application identification for private cloud and internet applications1.
- Self-service security with automation2 that provides centralized tagging, pre-defined security profiles, delegated firewall configurations and ingress web application security, enabling enterprises and service providers to secure AI deployments without operational complexity or fragmented security toolchains.
Customer and Partner Commentary
"Analyzing years of news archives in the public cloud is cost-prohibitive, with unpredictable pricing that makes AI projects difficult to plan," said V V Jacob, Senior General Manager, Systems for
"By unifying our VMs and containers on
“As enterprises move AI from experimentation to production, they need infrastructure that delivers performance, efficiency, and flexibility across a broad ecosystem at scale,” said
"Arista Networks and
“AI workloads are now prime targets, and recovery without validation is a risk enterprises can’t afford,” said
"
“Enterprises need infrastructure that delivers breakthrough AI performance while maintaining data sovereignty and control," said
Additional Resources
- Read all the
VMware Cloud Foundation 9.1 blogs to learn about the new innovations - Learn more about
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About
1-Based on internal
2-Advanced Service for VCF sold separately
Media Contact:
VMware Cloud Foundation Division,
roger.fortier@broadcom.com
+1.408.348.1569
Source: Broadcom Inc.