NETINT Technologies is the creator of the VPU (video processing unit) category, offering ASIC-based video encoding solutions that dramatically reduce energy consumption and operational costs by 20-40x compared to CPU-based encoding. The company's hardware supports advanced codecs including AV1, 8K, and HDR, and includes on-chip AI engines for image processing. NETINT has been recognized with a Technology & Engineering Emmy® Award for its efficient hardware video accelerators, and has partnered with Akamai to deploy VPU-powered video encoding in the cloud, demonstrating its credibility in hyperscale media processing.
Latin American OTT platform Zapping migrated its live streaming infrastructure from CPU and GPU servers to NETINT VPUs. The transition reduced the company's server footprint by 75% and lowered encoding power consumption by 90% while supporting low-latency delivery for live sports.
Zixi and NETINT have published a technical overview detailing the integration of Zixi's software-based transport and control plane with NETINT's hardware-based video processing units. The collaboration aims to improve live streaming workflow reliability and transcoding density by offloading processing tasks from general-purpose CPUs to purpose-built hardware.
Purpose-built silicon the open market deploys to encode video at scale economically.
NETINT and i3D.net have announced a partnership to integrate NETINT's Quadra video processing units (VPUs) into i3D.net's managed bare metal and private cloud infrastructure. The collaboration aims to provide dedicated hardware-based encoding for latency-sensitive workloads such as cloud gaming and interactive streaming.
NETINT Technologies has released a seven-step technical framework for migrating streaming video workloads from CPU-based processing to their Quadra Video Processing Units (VPUs). The guide provides engineering teams with recommended strategies for validating hardware, ensuring service equivalence through feature parity, and establishing reliable rollback paths during the transition.
NETINT Technologies explains the operational advantages of using dedicated video processing units (VPUs) over general-purpose CPUs for video encoding pipelines. The technical breakdown details how keeping decode, scaling, and encode stages on a single hardware device reduces data bus contention and increases aggregate throughput for multi-session streaming workflows.
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.
NETINT CMO Mark Donnigan argues that media companies should prioritize video processing units (VPUs) over general-purpose CPUs for large-scale encoding workloads to improve density and power efficiency. The article highlights the company's Quadra T1U hardware as a solution for achieving higher streams per server in modern data centers.
This report from NETINT surveys video encoding professionals on current and future trends including the adoption of AV1, the shift toward hybrid infrastructure, and growing integration of machine learning. The data highlights a transition away from GPU-only hardware and emphasizes that budget and capacity constraints remain the primary barriers to adoption for most organizations.
NETINT Technologies introduced its Quadra VPU product family, utilizing the company's Codensity G5 ASICs for hardware-based AV1, HEVC, and H.264 encoding. The product line is designed to replace CPU-based transcoding in data center, edge, and cloud gaming environments to increase stream density and reduce power consumption.
NETINT published a technical blog post arguing that AV1 adoption at scale requires moving encoding workloads from CPUs to purpose-built video silicon. The post cites Quadra VPU benchmarks and quality tests claiming lower watts per stream and bitrate savings versus CPU/GPU-based approaches, including AV1 savings at 1080p and higher density at rack scale.
