INNOVATION

Encoding Hardware Faces a New Rival in 2026

NETINT's 2026 survey finds VPU adoption intent at 51.5%, nearly matching GPUs, signaling a hardware shift in video encoding.

29 Jul 2026

Presentation slide comparing GPU training performance with a line graph and colored processor benchmark grids

Video encoding is entering a more competitive phase. A July 2026 NETINT survey of 286 industry professionals found specialized video processing units, or VPUs, nearly level with GPUs in planned adoption for 2026. VPUs reached 51.5 percent, while GPUs stood at 53.6 percent, suggesting a meaningful shift in how organizations are planning future encoding infrastructure.

Much of that momentum comes from growing frustration with traditional GPU deployments. Respondents ranked power consumption as the biggest concern at 39 percent, followed by codec limitations at 37 percent and stream density constraints at 35 percent. Together, those pressures are prompting engineering teams to rethink long-standing hardware choices, even as GPUs remain dominant in AI and general-purpose computing.

Instead of relying on a single platform, many organizations are matching hardware to specific workloads. Specialized processors are increasingly handling video encoding tasks where efficiency matters most, while GPUs continue serving broader compute demands. The result is a more flexible infrastructure strategy built around performance, operating costs, and output quality rather than brand familiarity.

For companies streaming at scale, the business case is straightforward. Lower power consumption can reduce operating expenses and shrink data center footprints, with savings multiplying across thousands of simultaneous video streams. Broadcasters, cloud providers, and streaming platforms all stand to benefit from more efficient hardware allocation.

The survey points to a maturing market where different processors excel at different jobs. As VPUs gain credibility alongside GPUs, procurement teams and infrastructure leaders have stronger evidence to diversify their encoding stacks and prioritize performance per watt over one-size-fits-all deployments.

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