The “AI Race” is often portrayed as a hunt for silicon. Enterprises scramble to secure high-performance GPUs, believing that raw compute power is the sole gatekeeper to innovation.
However, compute is only as fast as the network that feeds it.
Traditionally, procuring high-end GPUs (GPU-as-a-Service) and configuring the massive pipes needed to move data (Network-as-a-Service) were two entirely separate workflows.
This fragmentation created a “bottleneck effect” that delayed AI deployments by weeks or even months.
That’s why we have partnered with PacketFabric to launch an industry-first, operationalized solution that integrates on-demand GPU compute and on-demand networking into a single experience.
Read the full press release here.
For AI Training and Inference, Data is the Fuel
When you are working with petabyte-scale datasets, you need a network that can handle massive throughput with ultra-low latency, and you need it to scale exactly when your compute scales.
By integrating these two pillars, organizations will be able to manage their entire AI infrastructure stack from the PacketFabric portal.
How It Works: A Unified AI Stack
This integration removes the technical complexity of connecting high-performance compute to your data sources. Instead of coordinating between different vendors and manual network configurations, the process is now streamlined through a software-defined approach.
Through the PacketFabric portal and the PacketFabric interface, users can now provision these connections using natural-language commands, meaning you don’t need a specialized network engineer to link your data to your GPUs. Why does this integration matter for Enterprises:
1. Deployment Without the Friction
Traditionally, compute and networking operate in silos, leading to weeks of manual hand-offs and internal ticketing delays. This integration automates the middleman. When you spin up GPU capacity, the high-performance networking will already be there, ready to move data.
It replaces a complex project management cycle with a single operational workflow.
2. Data Gravity is No Longer a Blocker
AI projects often stall because the data is in one place and the compute is in another. Moving terabytes of training data across the public internet is insecure and slow. This solution provides a private, high-capacity “express lane” between your data centers, various clouds, and your GPUs.
This helps to ensure your expensive compute resources are never sitting idle waiting for data.
3. Financial Agility
Hardware procurement usually requires long-term capital commitments and rigid forecasting. However, AI needs are unpredictable. This model shifts infrastructure to an on-demand expense, allowing you to scale up for intensive training and scale back for inference without being locked into depreciating assets or unused capacity.
4. Simplified Governance and Oversight
Managing separate vendors for compute and connectivity creates visibility gaps and troubleshooting headaches. By unifying these layers into one portal, your team gets a clear, consolidated view of performance and spend.
It’s one point of control for the entire AI infrastructure stack, making it easier to audit, manage, and optimize.
Going From Idea to Production-Ready AI
The ultimate goal of any AI strategy is Time to Value. Every day spent configuring routers or waiting for hardware delivery is a day your competitors gain an edge.
By combining Massed Compute’s elastic NVIDIA GPU power with PacketFabric’s intuitive, AI-powered networking, we have removed the final friction points in the AI lifecycle.
Ready to accelerate your deployment? Contact us.
