Bridge by Armada now integrates with Dataiku, the enterprise AI platform data teams use to prepare data, build models, and govern AI projects. Neoclouds and sovereign GPU cloud providers that use Bridge can now offer Dataiku to their tenants directly from the Bridge Marketplace, running on the provider's own GPU infrastructure.
On October 13, NVIDIA released the complete Run:ai platform as open source. KAI Scheduler, the scheduling engine behind Run:ai, has been open source since 2025. The new release adds the rest of the platform:
Production-grade GPU orchestration no longer requires a license.
For neoclouds and sovereign cloud providers building on Armada Bridge, that's good news. Bridge already integrates with commercial NVIDIA Run:ai, and the same integration now extends to the open source release. Tenants who want Run:ai can get it without a procurement step, and every Bridge deployment gets a lower total cost of ownership.
Bridge from Armada is now available on the NVIDIA DSX Air Demo Marketplace. Anyone with a DSX Air account can launch a complete Bridge GPUaaS environment in simulation and run it end to end, from Infrastructure discovery to tenant workloads, without waiting on a single server. To simplify the experience, AI-as-a-Service features have been disabled in this environment.
Bridge is Armada's GPU cloud management software. Neoclouds, sovereign clouds, telcos, and enterprises use it to turn GPU clusters into a multi-tenant GPU resource with cloud services (IaaS, PaaS, and AIaaS) layered on top. Bridge also includes user management, RBAC, and billing.
Inference has overtaken training as the dominant AI workload, consuming two-thirds of all AI compute in 2026. Enterprises now need to execute always-on inference and fine-tuning at large scale, and serving that demand requires a highly efficient AI services platform that can cater to hundreds of customers on a single cloud infrastructure.
How Armada's GPU-as-a-Service Management Platform Orchestrates AI Token Factories at Scale
The phrase "token factory" has moved from marketing metaphor to operational reality. Neoclouds and sovereign cloud providers are assembling clusters of hundreds — or thousands — of GPU nodes, connecting them over high-speed interconnect fabrics, shared across dozens of tenants simultaneously: each with their own isolated network, dedicated storage quota, optional private NVLink or IB partition, and bespoke mix of training, fine-tuning, and inference workloads.