Why CPU-Based Video Encoding No Longer Scales Efficiently
CPU transcoding still plays an important role in offline encoding, codec experimentation, and highly customized media workflows. But continuous real-time streaming environments expose the limitations of software-only encoding very quickly.
Modern codecs such as HEVC and especially AV1 require significantly more compute per frame. Motion estimation, adaptive quantization, rate-distortion optimization, in-loop filtering, and look-ahead analysis create workloads that push CPU architectures beyond what they were originally designed to handle efficiently.
At smaller scale, this remains manageable. At production scale, several bottlenecks appear simultaneously:
- memory bandwidth limitations
- rising power consumption
- declining stream density
- increased cooling requirements
- inefficient rack utilization
The economics become even more difficult when operators attempt real-time AV1 deployment using CPU-only infrastructure.
NETINT benchmarking showed that a single Quadra-enabled server can replace dozens of standalone CPU systems depending on codec and preset configuration. In x265 Medium scenarios, software-only infrastructure may require more than 55 CPU systems to match the throughput of a single hardware-accelerated Quadra deployment.
For engineering teams running:
- live OTT workflows
- cloud gaming
- surveillance systems
- contribution encoding
- edge delivery pipelines
…the challenge is no longer just performance.
It becomes an infrastructure scaling problem.
Why ASIC-Based VPUs Change the Video Infrastructure Equation
One of the biggest misconceptions in video infrastructure is treating CPUs, GPUs, and VPUs as interchangeable acceleration models.
They are not.
GPUs were designed primarily for graphics rendering and parallel compute. Their encoding engines are powerful, but they still operate inside architectures optimized for graphics workloads.
VPUs take a different approach.
The NETINT Quadra family uses ASIC-based video processing architectures purpose-built for:
- video encoding
- decoding
- scaling
- AI-assisted processing
- media optimization
- low-latency delivery
Instead of allocating part of the silicon to video, the architecture is dedicated almost entirely to media workloads.
That specialization matters at scale.
The Codensity G5 ASIC inside the Quadra lineup supports:
- real-time AV1 encoding
- high-density HEVC and AVC transcoding
- sub-frame latency
- ROI encoding
- HDR workflows
- AI-assisted encoding
- continuous 24/7 operation
This allows operators to increase throughput while reducing:
- server count
- rack footprint
- cooling overhead
- power consumption
- operational cost
Under sustained workloads, CPUs waste power maintaining general-purpose compute resources. GPUs dedicate thermal and silicon budgets to rendering operations that many transcoding environments never use.
VPUs eliminate much of that overhead by focusing directly on video processing.
NETINT Quadra Product Line Overview
The Quadra family is designed to support multiple deployment models ranging from dense datacenter transcoding to edge encoding and embedded media systems.
Quadra Product Comparison
Source: NETINT Quadra product specifications and deployment documentation.
Although all Quadra products share core codec support and ASIC acceleration capabilities, each model targets different operational requirements.
The Quadra T1U prioritizes dense deployment efficiency in compact server environments. The Quadra T2A pushes throughput much further for hyperscale streaming and cloud gaming environments. The T1M extends hardware transcoding into low-power edge deployments where PCIe cards may not fit operationally or physically.
That flexibility is important because modern video infrastructure is increasingly distributed across:
- datacenters
- edge nodes
- cloud gaming regions
- surveillance hubs
- CDN edge infrastructure
- hybrid cloud architectures
Quadra T1U: High-Density Video Encoding in 1RU Infrastructure
Using a U.2 form factor with a single Codensity G5 ASIC, the Quadra T1U supports:
- up to 32 simultaneous 1080p30 streams
- up to 8x 4Kp30 streams
- up to 2x 8Kp30 streams
- approximately 17W power consumption
For live transcoding infrastructure, those numbers become especially important when multiplied across racks and datacenters.
The T1U supports:
- H.264
- HEVC/H.265
- AV1 Main
- HDR10
- HDR10+
- HLG
- VP9 decoding
- AI-assisted encoding
It also integrates directly with:
- FFmpeg
- GStreamer
- LibXcoder APIs
That compatibility matters because infrastructure teams rarely want proprietary transcoding pipelines that force major workflow changes.
The T1U allows operators to increase stream density while maintaining relatively low power consumption and operational overhead.
Quadra T2A: Ultra-Density Transcoding for AV1 and Cloud Gaming
The Quadra T2A targets environments where throughput and concurrency matter most.
Built with dual Codensity G5 ASICs, the T2A supports:
- 64x 1080p30 streams
- 16x 4Kp30 streams
- 4x 8Kp30 streams
- 36 TOPS AI acceleration
- sub-frame latency encoding
This makes the platform especially relevant for:
- cloud gaming
- hyperscale OTT
- low-latency streaming
- virtualized media infrastructure
- high-density contribution encoding
Cloud gaming is one of the clearest examples of where VPUs improve infrastructure efficiency. GPUs remain essential for rendering, but offloading encoding workloads to dedicated VPUs allows rendering resources to scale more efficiently.
As frame rates, resolutions, and AV1 adoption continue increasing, separating rendering and encoding becomes increasingly valuable for maintaining concurrency and controlling operational cost.
Quadra T1M: Bringing Hardware Transcoding to the Edge
Not every transcoding deployment lives inside a hyperscale datacenter.
Edge streaming, mobile infrastructure, and embedded deployments increasingly require localized video processing closer to end users.
The Quadra T1M addresses this with a compact M.2 form factor designed for low-power deployments.
The platform supports:
- AV1 Main
- H.264
- HEVC/H.265
- HDR10 and HDR10+
- ROI encoding
- metadata insertion
- closed captions
- look-ahead processing
Despite operating below 10W under load, the T1M still supports:
- 20x 1080p30 encode streams
- 5x 4Kp30 encode streams
- 25x 1080p30 decode streams
NETINT highlights edge deployment scenarios involving:
- dynamic ad insertion
- localized streaming
- cloud gaming edge nodes
- low-latency rendering pipelines
The T1M allows operators to deploy hardware transcoding in environments where traditional accelerator cards may not fit operationally or physically.
Real-Time AV1 Encoding Without GPU Overhead
AV1 adoption continues accelerating because of its compression efficiency and bandwidth savings. The challenge is that real-time AV1 encoding remains computationally demanding, especially at scale.
Software-based AV1 encoding quickly becomes difficult to scale economically. GPU acceleration improves throughput, but large deployments still face power, thermal, and density constraints.
Quadra VPUs were designed specifically to address this transition.
The Codensity G5 ASIC supports:
- real-time AV1 encoding
- multilayer AV1 workflows
- AI-assisted optimization
- flexible GOP structures
- customizable reference structures
- HDR workflows
This becomes especially important for:
- live OTT
- interactive streaming
- contribution encoding
- sports streaming
- edge delivery
- cloud gaming
Live environments expose infrastructure weaknesses quickly because workflows operate continuously under strict latency constraints.
The Quadra platform also supports:
- sub-frame latency
- dynamic bitrate control
- IDR insertion
- overlay processing
- scaling
- cropping
- RGB/YUV conversion
These capabilities directly affect live workflow stability and scalability.
AI-Assisted Encoding and Intelligent Video Processing
AI is becoming increasingly integrated into modern video infrastructure, but many deployments still struggle balancing inferencing workloads against transcoding density.
Quadra introduces a more efficient division of labor.
Instead of forcing GPUs to handle every stage of the AI pipeline, Quadra VPUs assist with:
- ROI detection
- AI-assisted encoding
- object detection support
- intelligent bitrate allocation
- preprocessing workflows
NETINT describes deployments where ROI coordinates generated during transcoding are fed into GPUs running more advanced AI models.
One surveillance deployment achieved:
- 48x 1080p30 decoded streams per VPU
- 480x 1080p30 streams per 1RU server
while simultaneously supporting:
- facial recognition
- object detection
- AI-assisted filtering
- timestamp overlays
- bitrate reduction
This combination of density and AI-assisted processing is especially valuable for:
- surveillance providers
- control centers
- video wall deployments
- large-scale monitoring systems
Real-World Cost Reduction Deployments
One of the strongest arguments for ASIC-based transcoding is operational economics.
The migration from CPU and GPU infrastructure to VPUs is often driven by:
- power costs
- server density limitations
- rack expansion constraints
- cooling overhead
- cloud infrastructure expenses
Several Quadra deployments demonstrate these savings clearly.
Source: NETINT Quadra production use cases
These are not isolated benchmark exercises. They represent production-scale deployments handling continuous transcoding workloads and live traffic.
FFmpeg and GStreamer Integration
One reason Quadra adoption is easier than many engineers expect is software compatibility.
The platform integrates with:
- FFmpeg
- GStreamer
- LibXcoder APIs
That allows infrastructure teams to accelerate existing pipelines without redesigning entire transcoding architectures.
For engineering organizations already invested in FFmpeg-based workflows, this matters enormously because operational migration becomes incremental rather than disruptive.
Quadra also supports:
- Linux
- Windows
- macOS
- Android
- x86 servers
- Arm-based servers
This flexibility allows deployment across cloud, edge, embedded, and enterprise environments.
The 2026 Decision Framework
Video infrastructure decisions are becoming less about raw compute and more about operational efficiency. The growth of AV1, interactive streaming, AI-assisted workflows, and ultra-dense live delivery has fundamentally changed how encoding platforms are evaluated.
For engineering teams planning infrastructure refresh cycles over the next 12 to 24 months, the decision framework is becoming increasingly straightforward.
1. Is the workload continuous?
If transcoding operates 24/7, power efficiency and stream density immediately become primary constraints. CPU-based encoding may still work for experimental or low-volume workflows, but continuous live delivery environments typically benefit far more from dedicated hardware acceleration.
This includes:
- OTT streaming
- surveillance
- conferencing
- cloud gaming
- contribution encoding
- CDN edge processing
The longer workloads run continuously, the faster ASIC-based acceleration improves infrastructure economics.
2. Is rack density becoming a problem?
Many streaming operators are no longer constrained purely by compute performance. They are constrained by:
- rack space
- cooling
- datacenter expansion costs
- power availability
This is where VPUs become especially valuable.
Quadra deployments allow significantly higher stream density while reducing:
- server footprint
- thermal output
- power-per-stream costs
For organizations trying to scale without expanding datacenter infrastructure, this becomes a major architectural advantage.
3. Is AV1 part of the roadmap?
This is increasingly the deciding factor.
AV1 dramatically improves compression efficiency, but real-time deployment at scale can overwhelm CPU-based systems. GPU acceleration improves throughput, but often increases infrastructure cost and power consumption.
Purpose-built VPUs such as Quadra were specifically designed to make large-scale AV1 deployment economically practical.
For many operators, AV1 adoption is no longer a future project. It is an active infrastructure requirement.
4. Does the workflow require low latency?
Interactive streaming environments expose infrastructure weaknesses quickly.
Cloud gaming, conferencing, remote production, and edge streaming all require:
- deterministic performance
- low jitter
- low latency
- stable throughput under load
Quadra VPUs support sub-frame latency encoding while maintaining high-density throughput, making them well suited for these environments.
5. Is operational cost becoming difficult to scale?
The biggest infrastructure challenge for many operators is no longer initial deployment cost. It is long-term operational scaling.
Power, cooling, hardware refresh cycles, and rack expansion costs compound quickly across large transcoding fleets.
Real-world Quadra deployments have already demonstrated:
- multi-million-dollar OPEX reductions
- lower hardware footprint
- improved stream density
- lower cost-per-stream economics
For many organizations, ASIC-based VPUs are no longer simply a performance optimization. They are becoming a necessary infrastructure strategy for sustainable scaling.
Conclusion
The video infrastructure market is entering a new phase where efficiency matters as much as codec quality. AV1 adoption, rising energy costs, denser streaming workloads, and low-latency requirements are exposing the limitations of CPU-centric transcoding architectures.
NETINT Quadra VPUs were built specifically for this transition.
By using purpose-built ASIC acceleration optimized for:
- real-time encoding
- dense transcoding
- AI-assisted workflows
- continuous operation
Quadra enables:
- higher stream density
- lower power consumption
- smaller server footprints
- scalable AV1 deployment
- lower operational cost
- low-latency streaming infrastructure
As video workloads continue growing, the industry is steadily moving toward specialized media acceleration architectures that maximize streams per watt instead of simply adding more CPU cores.
Where to Go From Here
The transition from CPU-centric transcoding toward specialized video acceleration is already underway across streaming, cloud gaming, surveillance, and AI-assisted media infrastructure.
The next step for engineering teams is evaluating which deployment model best aligns with operational goals, codec roadmaps, and scalability requirements.
Engineering teams evaluating migration from CPU or GPU transcoding infrastructure should prioritize:
- stream-per-watt efficiency
- AV1 readiness
- latency requirements
- rack density
- operational scalability
- software ecosystem compatibility
As streaming workloads continue increasing globally, specialized VPUs are becoming foundational infrastructure components rather than optional accelerators.
The shift is not simply about encoding faster.
It is about building video infrastructure that remains economically sustainable as demand, resolutions, concurrency, and codec complexity continue growing.
Frequently Asked Questions
What is the difference between Quadra T1U and T2A?
The T1U uses a single Codensity G5 ASIC and supports up to 32x 1080p30 streams at approximately 17W power consumption. The T2A uses dual ASICs and supports up to 64x 1080p30 streams with significantly higher throughput designed for hyperscale deployments, cloud gaming, and ultra-dense streaming environments.
Does NETINT Quadra support AV1 encoding?
Yes. Quadra VPUs support real-time AV1 Main profile encoding and decoding. The platform was specifically designed to enable scalable AV1 deployment with significantly better density and power efficiency than CPU-only transcoding infrastructure.
Can Quadra integrate with FFmpeg?
Yes. Quadra integrates directly with FFmpeg, GStreamer, and LibXcoder APIs, allowing engineers to accelerate existing transcoding pipelines without rebuilding their media workflows.
Is Quadra better than GPU encoding for 24/7 transcoding?
For many continuous transcoding workloads, ASIC-based VPUs provide better stream-per-watt efficiency and higher density because the architecture is dedicated specifically to video processing. GPUs remain valuable for rendering and general compute, but VPUs optimize infrastructure specifically for media acceleration.
What workloads benefit most from Quadra VPUs?
Quadra performs especially well in:
- live OTT streaming
- cloud gaming
- surveillance video analytics
- contribution encoding
- conferencing
- edge transcoding
- ABR ladder generation
- CDN video processing
Does Quadra support HDR workflows?
Yes. Quadra supports HDR10, HDR10+, and HLG workflows for H.264 and HEVC encoding and decoding.
Can Quadra run in Arm servers?
Yes. Quadra supports both x86 and Arm-based server deployments depending on the model and infrastructure configuration.
What makes VPUs different from CPUs?
CPUs are designed for general-purpose compute workloads, while VPUs are purpose-built for video encoding, decoding, scaling, and media processing. This specialization allows significantly better stream density and lower power consumption for video workloads.
Is Quadra suitable for edge deployment?
Yes. The Quadra T1M was specifically designed for edge and embedded deployments using an M.2 form factor with low power consumption and high-density transcoding capabilities.
How much infrastructure reduction can Quadra provide?
- more than 50% hardware reduction
- millions in operational savings
- dramatically lower transcoding costs
- significantly reduced power consumption
Sources & Further Reading
- NETINT Quadra Product Family
- NETINT/Akamai Throughput and Quality Report
- NETINT Quadra vs NVIDIA T4 Benchmarking Hardware Encoding Performance
- The Power of VPUs Compared to CPUs
- More Streams, Less Power: Energy Trade-offs in Video Infrastructure
- NETINT Codensity G5 Technology Overview
