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Learn how shared AI compute and multitenancy cut costs, speed training, and scale securely with AMD Instinct GPUs, TensorWave, ROCm, and enterprise-ready tools.

Move from Idea to Impact Faster with Shared AI Compute

AI isn’t just in labs anymore, it’s in everyday products and operations. To make it work, companies need an easy way to run and manage it, and running it well matters. The trouble is, most tech and budgets aren’t built to move at AI speed. From small businesses to enterprises, compute is now strategic, but unlike data, you can’t just copy it, you have to share it, plan it, and keep it reliable for everyone. 


How to Make Shared AI Work

Multitenancy lets many users or teams share the same power

AI isn’t just in labs anymore, it’s in everyday products and operations. To make it work, companies need an easy way to run and manage it, and running it well matters. The trouble is, most tech and budgets aren’t built to move at AI speed. From small businesses to enterprises, compute is now strategic, but unlike data, you can’t just copy it, you have to share it, plan it, and keep it reliable for everyone. 


How to Make Shared AI Work

Multitenancy lets many users or teams share the same powerful infrastructure; compute, storage, and networking. Multitenancy also keeps each group’s data and workloads separate and safe. It’s the model that makes cloud scale work. 

When usage spikes and high-demand users compete for resources, some issues arise. Real-time inference slows down with painful latency, and training or fine-tuning jobs lose throughput and finish late. In all of these cases, every GPU cycle counts, checkpoints are huge, and every second of downtime wastes money and time.

To be safe and prevent this from occurring, teams create their own GPU clusters and end up overdoing it. This means that GPU capacity goes unused, ideas stay stuck in silos, and the organization's costs climb.


A Simple Playbook for Shared AI

To solve the issue of AI multitenancy, AMD Silo AI, in collaboration with TensorWave and the Combient Group, have created a new model for shared AI compute infrastructure, designed to help teams build large-scale AI faster, at lower cost, and with better collaboration.

Powered by AMD Instinct™ accelerators on the TensorWave platform, plus AMD Resource Manager and AMD AI Workbench, the environment enables reliable GPU sharing across organizations. The result: easier access to advanced compute and a stronger pace of innovation.

“By advancing a more open, collaborative model for AI development, this initiative is making compute more accessible, elastic, and efficient. It empowers enterprises to innovate together in the next era of AI, driven by democratized access, scalable infrastructure, and accelerated time-to-value,” said Darrick Horton, Co-founder and CEO of TensorWave.


AI Compute Infrastructure for Multitenancy

A shared, multi-tenant design helps companies move faster together. It reduces idle GPUs, accelerates model training, lowers spend, and offers a new, sovereign-friendly way to run AI at scale. This goes for both enterprise size organizations and cloud providers. 

Built on open standards and open-source tech, the platform helps organizations reduce vendor lock-in and lets you move models and workflows easily across different environments. You also get a fast-moving toolkit that simplifies infrastructure and boosts performance.

Running on AMD Instinct™ GPUs and tuned for them end-to-end, this setup is a strong base for fast, reliable multitenancy, whether you’re on-prem or in the cloud.

 * AMD Instinct™ MI300X / MI325X: Created for large-scale AI, with hardware partitioning and high-bandwidth links.
 * AMD ROCm™ software stack: Supports GPU partitioning, containers, and works with Kubernetes, OpenShift, and Slurm.
 * AMD Infinity Fabric™ + advanced memory: Keeps workloads isolated and throughput high.
 * Validated ecosystem: Orchestration and schedulers proven for multi-tenant GPU clusters.
 * AMD AI Workbench & Resource Manager: Open-source, enterprise tools to deploy and scale AI with modular building blocks.


Powered by AMD’s Enterprise-Grade AI Tools

The Combient network brings together Nordic enterprises in manufacturing, energy, and finance to explore new technologies. Through this program, member companies get access to a shared, AMD-based AI infrastructure so they can co-develop and roll out advanced AI by:

 * Efficiently scaling AI workloads
 * Building and deploying models faster
 * Cutting environmental and financial waste from underused compute

With the enterprise-ready AI software stack from AMD Silo AI, plus AMD AI Workbench and AMD Resource Manager, built to run AI at scale, Combient companies can go from experiments to large-scale production faster and more efficiently. These tools provide access to leading open-source frameworks and GenAI models on an enterprise Kubernetes platform, speeding up every step of the journey.

“This model changes how our network of companies can access and scale AI.” said Jonas Wettergren, VP & Head of People & Org, at Combient. “Instead of each enterprise building its own infrastructure from scratch, they can now tap into a shared AI environment — cutting time-to-compute, lowering cost, and speeding up real-world deployment across 36 organizations, regardless of platform.”


What’s Next for Shared AI

As AI adoption speeds up, shared compute is set to shape the next generation of enterprise infrastructure—bringing collaboration, sovereignty, and efficiency together.

“Our collaboration with TensorWave and Combient shows that AI compute doesn’t have to be siloed,” says Peter Sarlin, CEO & Co-founder of AMD Silo AI. “Through shared, robust multi-tenant clusters powered by AMD technology, companies can scale innovation faster while maintaining the performance and isolation modern AI workloads require.”

Combient leads a network of top Nordic enterprises from multiple industries, all working to accelerate digital transformation. Today, it spans 36 companies, €280 billion in combined turnover, and 1.4 million employees. Learn more at combient.com.


About TensorWave

TensorWave is the AI neocloud purpose-built for performance. Powered exclusively by AMD Instinct™ Series GPUs, we deliver high-bandwidth, memory optimized infrastructure that scales with your most demanding models–training or inference. 

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Learn how shared AI compute and multitenancy cut costs, speed training, and scale securely with AMD Instinct GPUs, ROCm, and enterprise-ready tools.

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Move from Idea to Impact Faster with Shared AI Compute

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