Canonical, the company behind Ubuntu, announced official support for the NVIDIA Rubin platform, including NVIDIA Vera Rubin NVL72 rack-scale systems, alongside new distributions tied to NVIDIA’s Nemotron-3 open model family. The message is aimed squarely at enterprises moving from pilots to production-scale AI: once workloads grow into full-blown “AI factories,” the operating system stops being background plumbing and becomes a key part of performance, security, and repeatability.

Canonical’s pitch is that Ubuntu can serve as the stable, high-performance software substrate that brings together the major components of NVIDIA’s next platform cycle—Vera CPU, Rubin GPU, and BlueField-4 DPU—into a cohesive execution environment. In short: fewer integration headaches, faster time to production, and a foundation that’s familiar to organizations already standardized on Ubuntu across cloud and on-prem deployments.

A “trusted foundation” as AI infrastructure scales up

In the announcement, NVIDIA framed the partnership as a practical step toward making AI deployments easier to industrialize. Justin Boitano, Vice President of Enterprise AI Products at NVIDIA, argued that democratizing AI requires more than powerful silicon—it requires a secure, low-friction environment from cloud to edge. Canonical, for its part, positioned the collaboration as consistent with its own goal of making AI more accessible, particularly for companies building private or sovereign cloud environments where compliance, governance, and operational control are part of the decision.

That emphasis on “sovereign” isn’t accidental. In many European and regulated-industry contexts, organizations want the performance benefits of cutting-edge accelerators, but they also want clear security boundaries, predictable operations, and fewer opaque dependencies. Canonical is signaling that Ubuntu is meant to be the OS layer that enterprises can standardize on while the underlying hardware evolves rapidly.

Arm becomes a first-class citizen in Ubuntu 26.04

One of the most technical—and strategically important—parts of the announcement centers on Ubuntu 26.04 and Canonical’s commitment to treating Arm as a first-class architecture, with what it describes as performance parity with x86 for key use cases. That matters because NVIDIA’s Vera CPU is described as custom Arm-based, and the Vera Rubin NVL72 concept leans into a tightly integrated, rack-scale design where CPU, GPU, and networking/security acceleration must work together cleanly.

Canonical highlighted its work to integrate upstream capabilities including:

Nested virtualization, for layered virtualization use cases common in cloud environments and sophisticated test setups.
MPAM (Memory System Resource Partitioning and Monitoring), enabling providers to partition memory bandwidth and cache at the hardware level—an increasingly relevant tool for multi-tenant AI infrastructure where predictable performance is a requirement, not a luxury.

Canonical also pointed to deeper ecosystem alignment: it says this Arm-centric infrastructure will be reinforced with native Arm support in OpenStack Sunbeam and Apache Spark, aimed at enabling end-to-end data pipelines on Arm-native silicon without awkward workarounds.

Ubuntu as the host OS for NVIDIA Mission Control

Beyond raw compatibility, Canonical emphasized Ubuntu’s role as the host operating system for NVIDIA Mission Control, software intended to accelerate the operational side of large AI deployments—from initial configuration and deployment integration to ongoing cluster and workload management.

This is an important subtext: the market is moving away from simply “buying GPUs” and toward buying operationally manageable AI platforms. If Mission Control becomes a standard layer for running Rubin-era infrastructure, then being the clean, supported host OS underneath it gives Ubuntu a strong position in enterprise rollouts.

“Inference snaps” to tame model deployments

Canonical also addressed a pain point that shows up in nearly every real-world AI deployment: dependency sprawl and version conflicts when shipping LLMs across environments. The company introduced inference snaps as a way to package silicon-optimized AI models with the runtimes and libraries they need, in a containerized, immutable bundle that’s installed with a single command.

In parallel, Canonical said it will collaborate with NVIDIA on packaging and distributing the Nemotron-3 family of open models, starting with Nano variants. The broader idea is to reduce the gap between “it works on this machine” and “it runs reliably across the fleet,” especially as organizations try to standardize inference across many servers and sites.

BlueField-4 and storage: performance is a data pipeline problem, too

Rubin-scale AI doesn’t live or die on GPU horsepower alone. The data path—storage, networking, and the security envelope around it—can become the real bottleneck.

Canonical reiterated its commitment to NVIDIA BlueField-4 as a core networking and security component for NVIDIA platforms. In Canonical’s description, BlueField-4 brings substantial throughput and includes 64 NVIDIA Grace CPU cores, and Ubuntu is positioned as a foundational environment for NVIDIA DOCA, enabling offload of networking, storage, and security tasks from the main CPU to the DPU.

On the storage side, Canonical said it is working with partners to optimize Ubuntu’s storage subsystem for GPUDirect Storage with BlueField-4—aiming to improve high-speed access between NVMe storage and GPU memory, reducing bottlenecks that can throttle training and inference at scale. For on-prem and sovereign deployments in particular, Canonical framed the Ubuntu + BlueField-4 combination as a way to support secure multi-tenant AI infrastructure, including bare-metal isolation and hardware-root-of-trust concepts.

Why this matters now

The biggest takeaway isn’t a single feature—it’s the direction of travel. AI infrastructure is being built less like a collection of parts and more like a full-stack platform where compute, networking, storage, security, and operations are tightly coordinated. Canonical is making a clear bet: if NVIDIA Rubin becomes a major platform for next-generation AI factories, Ubuntu wants to be the default OS foundation enterprises rely on to deploy it quickly and safely.

FAQ

What does “official Ubuntu support” for NVIDIA Rubin actually mean?
It means Canonical is committing to validated compatibility and an enterprise-grade support posture so organizations can deploy Rubin-based systems on Ubuntu with fewer integration and operational risks.

Why is MPAM important for multi-tenant AI clusters?
MPAM allows hardware-level partitioning and monitoring of memory bandwidth and cache resources, which can help keep performance more predictable when multiple tenants or workloads share the same infrastructure.

What are “inference snaps,” and who benefits from them?
Inference snaps package models and their dependencies in an immutable, installable bundle. They’re designed to reduce dependency conflicts and make deployments more repeatable across many machines—especially useful for enterprises scaling inference.

Why are BlueField DPUs and GPUDirect Storage a big deal for AI?
Because AI performance depends heavily on data movement. Offloading networking/security/storage tasks and improving the NVMe-to-GPU path can reduce bottlenecks that otherwise waste expensive accelerator time.

Sources: Canonical announcement at CES 2026 (Ubuntu support for NVIDIA Rubin and Nemotron-3 distribution details).