VPU shift over software: Priority for encoding moves to hardware architecture
NETINT provides a technical argument for prioritizing silicon architectural selection over software-based encoder choice for video processing workflows. The analysis advocates for a heterogeneous compute model where purpose-built VPUs handle intensive video encoding to improve density and power efficiency relative to traditional CPU-based implementations.
Key Takeaways
- CPU-based HEVC encoding at 32 sessions averaged 2.47 FPS per session, whereas the NETINT VPU achieved 18.99 FPS in the same environment.
- The NETINT Quadra T1U consumes only 17 watts while supporting up to 32 parallel 1080p30 streams or two 8K streams across H.264, HEVC, and AV1.
- A heterogeneous compute model is recommended where the CPU manages the control plane while specialized VPUs handle dense media processing planes.
- Selection should follow a five-step sequence: define workload, service requirements, and operational model before choosing silicon and tuning encoders.
- Cloud abstraction does not eliminate silicon impact, as steady-state volumes carry high convenience premiums when mapped to inefficient general-purpose processors.
Why It Matters
This shift signals a maturation in streaming infrastructure where software-only flexibility is no longer the default for scaling. As platforms move toward AV1 and higher resolutions, the move from general-purpose CPUs to specialized VPUs like the Quadra T1U addresses specific bottlenecks in power consumption and rack density. For the broader ecosystem, this move toward hardware specialization mirrors the AI industry's reliance on GPUs, forcing DevOps teams to manage heterogeneous hardware stacks rather than homogeneous cloud instances. Watch for increased VPU-as-a-Service availability across major CDNs as they seek to lower TCO for high-volume live and VOD workloads.
Additional Context
The push toward specialized silicon comes as hardware-accelerated encoding gains significant traction. Per Bitmovin and NETINT research from April 2026, GPU and VPU solutions have reached a combined adoption rate of approximately 72% for hardware acceleration. While NVIDIA still holds a dominant 92% share of the broader data center GPU market as of early 2024 per research from arXiv, the video-specific segment is bifurcating. Operators are increasingly looking at energy efficiency as a key metric to meet Scope 3 emissions targets; recent Akamai benchmarks from August 2025 showed VPUs delivering up to 4.7x higher energy efficiency than high-performance GPUs in demanding media workloads. Integration with major cloud providers is also accelerating the transition. Per Akamai, the company launched its first VPU-powered Accelerated Compute instances in 2024 and expanded to a high-density 8-card plan in April 2026 to support massive-scale transcoding. This infrastructure shift coincides with a major codec transition; per the 2026 State of Video Encoding Report, AV1 is projected to reach 57% combined market reach by year-end. This growth is supported by wide device support, including Apple's iPhone 15 and Intel Arc Pro GPUs, which have lowered the barrier for at-scale AV1 adoption through native hardware decoding. Major streaming services are already optimizing their pipelines for these hardware constraints. Netflix reported in April 2026 that its move to Variable Bitrate (VBR) for live events required a complete reassessment of delivery and capacity management to handle the resulting traffic unpredictability. As the industry moves away from legacy H.264 appliances, which still held roughly 40% of the market in 2025 per Market Research Future, the focus has shifted to building flexible, workload-aware infrastructures that can selectively activate more efficient profiles like VVC and AV1 based on real-time device capabilities.
Read full article at netint.com
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