Mirantis

Can you actually prove that one tenant can't reach another tenant's data on your shared AI infrastructure? Most multi-tenant AI setups lean on software overlays and trust-based controls. That leaves the door open to noisy neighbors, cross-tenant data leakage, and isolation you can't demonstrate. On Aug 25, Shaun O'Meara, CTO at Mirantis, and Brad Burres, Senior Distinguished Engineer at NVIDIA, get into hardware-enforced multi-tenancy for bare-metal AI infrastructure, and why grounding isolation in hardware changes the guarantees you can make. Save your seat: https://buff.ly/hRRcdOu #AIFactory #AIInfrastructure #NVIDIA #Mirantis #GPU

Doing it yourself sounds like control. In AI infrastructure, it reads as risk. The hardware is the foundation but turning it into a stable, multi-tenant, hyperscaler-grade environment is where the real difficulty starts. In this edition's Expert Insight, Richard Borenstein, SVP of Business Development at Mirantis, breaks the DIY risk into three buckets — tech-stack complexity, the expertise gap, and operational efficiency — and explains why the teams pulling ahead don't rebuild the infrastructure; they let a proven platform carry it and pour their energy into what customers actually pay for. Happy reading! 💌 https://buff.ly/iXa2VPj

Multi-tenant AI infrastructure has gone from nice-to-have to a requirement, and AI factories are where that pressure shows up first. When you're running many tenants on shared GPU infrastructure at that scale, software overlays and trust-based controls stop being enough. Robust multi-tenancy has to be enforced in the hardware itself. On August 25, Shaun O'Meara, CTO at Mirantis, and Brad Burres, Senior Distinguished Engineer at NVIDIA, dig into the growing demand for multi-tenant AI infrastructure and how Mirantis and NVIDIA are delivering hardware-enforced multi-tenancy for AI factories together. Rama Darbha, Senior Director of Solutions Architecture, AI Factory Infrastructure at NVIDIA, moderates the conversation. Save your spot: https://buff.ly/NwhWDMn #AIFactory #AIInfrastructure #NVIDIA #Mirantis #GPU

Moving AI into production rarely breaks on hardware. It breaks on governance. Once models leave the lab, teams lose track of where they live and where inference requests actually go. Most end up stitching fragile tools together to fill the gap. Mirantis k0rdent AI closes it. The Model Registry gives your models, fine-tuned variants, and quantized builds one secure, OCI-native home across distributed infrastructure. The Inference Mesh routes, audits, and enforces policy on every request across models, regions, and clusters, so you can see where requests go and where the compliance gaps are. See how k0rdent AI brings enterprise-grade control to AI infrastructure: https://buff.ly/AgULaHy #Mirantis #k0rdentAI #NVIDIA #AIFactory #AIInfrastructure #AIInference

gVisor support is now available in Mirantis Container Runtime (MCR). Starting with the latest release, MCR officially supports gVisor as an OCI runtime alongside runc and crun. What this adds for container environments: • Stronger container isolation for multi-tenant and security-sensitive workloads • Support for multiple OCI runtimes on the same machine • OCI-compliant runtime support with minimal workflow changes • More flexibility in balancing security, compatibility, and performance requirements Learn more: https://buff.ly/Q0fMUts

Your data scientists have models ready to deploy. They shouldn't have to wait weeks for someone to stand up the environment. Mirantis k0rdent AI turns inference deployment from a specialist-gated process into self-service. Teams select pre-approved, template-defined stacks and provision governed environments in minutes, while built-in policy controls and multi-tenancy keep every workload isolated from a single control plane. See how teams ship AI services faster with k0rdent AI. https://buff.ly/N6z7x66 #Mirantis #k0rdentAI #NVIDIA #AIFactory #AIInfrastructure #AIPaaS

A full-stack AI factory should outlast the hardware you build it on today. The Mirantis AI Factory Reference Architecture maps all five layers on open standards, with certified NVIDIA GPU support in the stack, so the same blueprint carries across hardware generations. No replatforming from one generation to the next. One blueprint your teams reuse on every new deployment, instead of redesigning from scratch. Get the blueprint. Build it on k0rdent AI: https://buff.ly/EnBh7FX #Mirantis #k0rdentAI #NVIDIA #AIFactory #AIReferenceArchitecture

Most enterprises think they have an AI infrastructure strategy. What they actually have is a maintenance burden they haven't fully accounted for yet. In this edition, Richard Borenstein, SVP of Business Development at Mirantis, explains how composable architecture and the right partnership model enable enterprises to turn infrastructure investments into continuous evolution, without being overwhelmed by the complexity of maintaining them. Happy reading! 💌 https://buff.ly/qqfHzA7

Standing up an AI cloud usually means months of stitching together GPUs, Kubernetes, and AI services from different sources, piece by piece. Mirantis k0rdent AI, validated under NVIDIA's AI Cloud-Ready initiative, collapses that into one sovereign layer, from bare metal to running models. • Months of setup become days • One platform for GPUs, Kubernetes, and AI services • Your hardware, your data residency, your rules, with multi-tenant isolation • Run the GPUs you choose, NVIDIA, AMD, or Intel, anywhere you deploy See everything k0rdent AI does, from metal to model: https://buff.ly/NHiQgqx #Mirantis #k0rdentAI #NVIDIA #AIFactory #MetalToModel #AIInfrastructure