Edge / Distributed
Run high-density video encoding/decoding/transcoding inside your own data center using Quadra VPUs without changing ingest paths, codecs, or downstream workflows.
Use this architecture when:
- Latency must be minimized at capture or delivery points
- Network bandwidth to centralized infrastructure is constrained or costly
- Video is generated at scale across many locations
- Local processing is required before aggregation or distribution
This architecture is optimized for latency reduction, bandwidth efficiency, and geographic scalability.
What changes
- Encoding moves closer to video sources
- Network traffic is reduced before aggregation
- Latency-sensitive workloads improve measurably
What doesn’t
- Codecs, formats, or ingest standards
- Centralized storage, analytics, or playback systems
- Operational control or visibility
VPU placement
- Quadra VPUs are deployed at edge nodes or distributed sites
- VPUs handle encode/transcode only
- Central compute is relieved of video-heavy workloads
Scaling model
- Horizontal scaling via additional edge nodes
- No centralized bottlenecks
- Performance scales with physical footprint
Prerequisites
- Edge-capable Quadra VPU systems
- Network connectivity to central infrastructure
- Centralized monitor & orchestration layer
- Defined upstream aggregation or delivery endpoints
Validation path
- Deploy a single edge node with Quadra VPUs
- Measure latency, bandwidth reduction, and output quality
- Compare centralized vs edge-processed workloads
- Expand incrementally by location
What this is not
- Not a CDN replacement
- Not a centralized cloud architecture
- Not device-only software encoding
- Not dependent on AI inference pipelines
Outcome
Lower latency, reduced backhaul costs, and scalable video processing at the point of capture while preserving centralized control and visibility.
Supported by the VPU Ecosystem, partners operating this architecture in production today