As camera counts rise, resolutions increase, and AI analytics expand, backend infrastructure – not the camera – has become the true scalability bottleneck.
NETINT inserts a dedicated ASIC-based video processing layer between cameras and downstream systems. Operating after the camera and before storage, monitoring, or AI inference, our VPUs handle high-density decode, encode, and transcoding at scale, without replacing deployed cameras.
Modern surveillance architectures require higher stream density per server, predictable watts per stream, and clean separation between video processing and AI workloads. NETINT enables up to 10X more streams per NVR while reducing storage growth, GPU contention, and infrastructure cost.
Rather than scaling with general-purpose compute, NETINT improves efficiency at the video processing layer itself – increasing streams per rack unit and stabilizing backend economics as deployments grow.
Surveillance infrastructure demands measurable density and efficiency, not incremental CPU scaling.
A single Quadra-equipped server can process:
Up to 320 simultaneous 1080p streams
Over 1,000 SD streams
Up to 8 streams at 4K per ASIC
Approximately 17–20 watts per VPU
Traditional CPU-based systems require significantly more power and rack space to reach comparable throughput.
Security platforms and system architects face:
Rapid growth in camera counts and resolution
More cameras per site and the shift to 4K and higher resolutions multiply bandwidth and processing demands across the entire backend.
Exponential storage expansion and longer retention requirements
Always-on recording combined with compliance-driven retention policies drives continuous growth in storage footprint and long-term infrastructure cost.
Monitoring systems struggling to scale across hundreds of feeds
Delivering full-resolution streams to dashboards and control rooms consumes unnecessary bandwidth and infrastructure, limiting how many feeds operators can view simultaneously.
GPUs overloaded with both AI inference and video processing
Decode and encode workloads compete with AI inference, reducing GPU efficiency and inflating infrastructure requirements for large-scale analytics deployments.
Rising power, rack space, and cooling costs
Scaling by adding servers increases operational expense and data center pressure without improving efficiency per stream.
Fragmented camera ecosystems and inconsistent encoding profiles
Multi-vendor deployments introduce mixed codecs, bitrates, and compression settings that limit backend optimization and make system-wide standardization difficult after video is ingested.
AI deployments expanding without predictable cost control
As AI analytics scale across more cameras, bandwidth, GPU utilization, and cloud inference costs grow non-linearly, making large-scale AI adoption financially unstable without architectural optimization.
Throwing more CPUs and GPUs at the problem doesn’t fix the architecture.
NETINT operates between cameras and backend infrastructure, optimizing video before it reaches storage, monitoring, or AI systems. By introducing a dedicated ASIC video processing layer, surveillance platforms can reduce storage footprint, lower power consumption, and increase stream density without modifying deployed cameras.
Reduce storage footprint while increasing stream density per server.
Storage Optimization at Scale
Transcode and normalize video after ingestion to reduce storage footprint without modifying deployed cameras.
Low-Bitrate Proxy Streams for Monitoring
Deliver efficient proxy feeds for dashboards and control rooms while preserving full-resolution archival video.
AI Video Pre-Processing
Condition and optimize video before inference to reduce bandwidth, GPU load, and cloud compute cost.
GPU Offload for AI Scalability
Move decode and encode workloads off GPUs so inference infrastructure can scale predictably.
200,000+ VPUs deployed
1+ trillion minutes of video processed
Powering large-scale cloud, AI, and surveillance platforms
This conversation is designed for teams scaling surveillance infrastructure beyond pilot deployments
NVR manufacturers
VMS and video analytics providers
GPU infrastructure architects
Security system designers managing large-scale deployments
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