NETINT advocates VPUs over CPUs to solve high-density encoding constraints

The immediate implication is a move toward purpose-built silicon to decouple media processing from general-purpose compute, which is increasingly burdened by orchestration and application logic. In a market where power is the primary constraint on data center growth, transitioning to VPUs allows platforms to scale concurrent stream counts without proportional increases in rack space or cooling load. Watch for a rise in hybrid hardware strategies where VPUs handle high-volume ABR ladders while GPUs or high-end CPUs are reserved for specialized, quality-critical mezzanine processing.

The push for dedicated media silicon comes as the broader data center landscape faces unprecedented power scarcity. Per Gartner (June 2026), global data center electricity consumption is projected to reach 565 terawatt-hours in 2026, a 26% year-over-year increase driven largely by compute-intensive AI workloads. This surge has made 'power security' a critical competitive differentiator for hyperscalers and streaming enterprises, as utility providers in key regions report that grid interconnection timelines are slipping by 1.5 to 2 years beyond initial expectations, according to Bloom Energy (2026).

Infrastructure providers are increasingly moving away from x86 dominance to accommodate these energy constraints. Per reports from ByteIota (December 2025), ARM-based processors accounted for 50% of new hyperscaler compute capacity by the end of 2025. This shift is exemplified by Netflix, which reportedly saved $15 million annually by migrating significant portions of its video encoding fleet to ARM-based AWS Graviton instances, achieving 20% faster processing alongside a 30% reduction in compute costs.

Recent comparative benchmarks from Akamai (August 2025) further highlight the delta between specialized and general-purpose hardware. In production tests using the C21 Live Encoder, VPUs demonstrated a 4.7x higher energy efficiency rating than GPUs for comparable media workloads. As data centers scale toward gigawatt-level AI factories, streaming operators are being forced to treat encoding efficiency as an architectural baseline rather than a software tuning exercise, with best-in-class facilities now targeting a Power Usage Effectiveness (PUE) of 1.2 or lower to maintain profitability.