Video demand keeps climbing while the path to add infrastructure narrows. The teams that win at IBC 2026 will be the ones who stop treating capacity as a purchasing question and start treating it as an architecture question.
NETINT VPU ECOSYSTEM · IBC 2026
AT A GLANCE
Video teams are facing a new capacity challenge as demand for live streaming, cloud transcoding, and advanced codecs continues to grow while power, rack space, and infrastructure resources become increasingly constrained.
This article explains why video capacity is now an architectural problem rather than a purchasing decision, and how purpose-built VPU acceleration enables higher density, greater efficiency, and more scalable streaming infrastructure.
For most of the last decade, adding video capacity was a procurement exercise. Forecast the growth, buy the servers, rack them, and the economics took care of themselves. That model still exists, but it no longer fits the market every video team now operates in.
The pressure is not coming from one place. More live channels, more user-generated content, more short-form, more codecs, more renditions of every asset, and more cloud transcoding all push encoding demand up at once. Live makes it harder still, because the processing has to happen in real time and the event calendar does not move.
At the same time, the supply side has tightened in ways that have little to do with video. The AI build-out has pulled servers, memory, power, and data center planning toward high-density GPU systems. Even teams that will never buy an AI server are now buying in the same constrained market.
Two curves moving in opposite directions.
Figure 1. Two curves moving in opposite directions: video processing demand keeps rising while the practical capacity to add general-purpose servers flattens.
The constraint moved from compute to the grid
The clearest signal is power. A U.S. Department of Energy study from Lawrence Berkeley National Laboratory found that data centers consumed about 4.4 percent of U.S. electricity in 2023, and projected that share to reach somewhere between 6.7 and 12 percent by 2028. The International Energy Agency reaches a similar conclusion globally, with data center consumption set to roughly double from about 415 terawatt-hours in 2024 to around 945 by 2030.
Capacity that exists on paper is not the same as capacity you can energize. Industry analysts now cite grid interconnection waits of roughly four years across much of the United States, a figure corroborated by Berkeley Lab and the IEA. For a video team, that is not a distant macro statistic. It is the reason a new rack of servers can be approved and still sit idle.
The grid is now the constraint
Figure 2. Data center electricity demand is set to roughly double this decade, and AI is the named driver in every major forecast.
The old model creates new bottlenecks
Server-based expansion now runs into four walls. Procurement can be slow as component allocation and budget cycles stretch out. Even when hardware arrives, power, cooling, and rack space can limit where it can be deployed. GPU supply is its own constraint: by early 2026, analysts at SemiAnalysis described on-demand GPU rental as sold out across all types, with memory suppliers reporting HBM capacity booked through the year.
CPU-only encoding compounds the problem. General-purpose processors are flexible, but at scale they consume power and rack space quickly, and the newest codecs make this worse rather than better. A peer-reviewed IEEE study found that software encoding with AV1 or VVC can be tens to hundreds of times more compute-intensive than H.264. The codecs that save bandwidth cost far more to produce in software, which is exactly the gap purpose-built hardware closes.
So the better question is not what can encode video. It is what can encode video efficiently, predictably, and at the density the business actually requires.
Why purpose-built video processing matters now
A video processing unit is built for one job. NETINT’s VPUs are ASIC-based accelerators designed for high-density, low-power encoding and transcoding, rather than video bolted onto general-purpose silicon. An independent benchmark published by Akamai and Cires21 measured a NETINT Quadra VPU against a comparable GPU on a full adaptive-bitrate ladder and found roughly 4.7 times the energy efficiency, with the VPU drawing about 13 watts where the GPU drew 82.
That efficiency is not only an electricity-bill story. It changes rack density, deployment timelines, capital planning, and how much capacity a team can stand up before the next infrastructure condition has to be solved. In a constrained market, efficiency is optionality.
The platforms that win will not be the ones that buy the most servers. They will be the ones that find smarter ways to process more video from where they already are.
The answer is not one path
There is no single fix, because there is no single bottleneck. Some teams need capacity before a build can finish, and are better served by hosted VPU capacity through an infrastructure partner. Some already own servers that are underused or aging, and can evaluate them for VPU acceleration once power, thermals, and PCIe are validated. Others need owned infrastructure built specifically for video, and should design dedicated VPU systems from the start.
This is why the ecosystem matters. NETINT provides the VPU foundation. Partners provide the paths to access it, build with it, validate it, and connect it to real workflows. At IBC 2026, the NETINT VPU Ecosystem Pavilion is organized around exactly those three paths.
The most useful conversation at the show will not begin with how many servers a team can buy. It will begin with one specific constraint, and a practical question: what is the most efficient way to add video capacity from where we are today?
Technical sources
Lawrence Berkeley National Laboratory, 2024 U.S. Data Center Energy Usage Report (DOE-commissioned, Dec 2024). https://eta-publications.lbl.gov/sites/default/files/2024-12/lbnl-2024-united-states-data-center-energy-usage-report.pdf
International Energy Agency, Energy and AI (2025). https://www.iea.org/reports/energy-and-ai/executive-summary
SemiAnalysis, The Great GPU Shortage (Apr 2, 2026). https://newsletter.semianalysis.com/p/the-great-gpu-shortage-rental-capacity
IEEE, Energy Consumption and Carbon Emissions of Modern Software Video Encoders (2023). https://ieeexplore.ieee.org/document/10374545/
Akamai, Benchmarking VPUs and GPUs for Media Workloads (Aug 19, 2025). https://www.akamai.com/blog/developers/benchmarking-vpus-and-gpus-for-media-workloads
NETINT VPU ECOSYSTEM · IBC 2026
As video workloads continue to grow, adding capacity is no longer a single decision, but a set of practical paths depending on the real bottleneck. This series explores how to scale video processing efficiently using NETINT VPUs, whether through infrastructure partners, existing systems, or dedicated deployments designed around video from the start.
1. The New Video Capacity Problem
2.Three Ways to Add Video Capacity
3. VPU-Powered Capacity as a Service
4. Get More from the Servers You Already Own
5. Build Dedicated VPU-Based Video Infrastructure.
6. VPU Capacity Without a Hardware Cycle
7. Scaling Performance-Critical Video Workloads
Coming soon:
8. Building Practical VPU-Based Video Systems
9. Reliable Live Video Meets Efficient Processing
10. Maximizing Encoding Efficiency in Live Streaming Workflows
11. The Next Layer of Video Efficiency
12. From VPU Evaluation to Production Deployment
