Deployment FAQs & Runbook
Common Issues, Causes, and What to Check First
Use this guide to quickly isolate issues in production, pre-production, and hybrid deployments.
1. Why isn’t the VPU being detected by the OS or application?
Most likely causes: Initialization, BIOS, or driver alignment.
Check:
- PCIe slot enabled and correctly sized
- BIOS settings (lane allocation, SR-IOV if required)
- Firmware and driver versions match
- OS-level enumeration (lspci, device manager)
Rule: If the OS doesn’t see the card, the application won’t either.
2. What causes performance to be lower than expected?
Most likely causes: System configuration, not hardware limits.
Check:
- Encoder and codec profile settings
- Host CPU capacity feeding the VPU
- NUMA alignment and PCIe placement
- Workload matches intended VPU use case
Rule: VPUs deliver predictable performance when the surrounding system is tuned correctly.
3.What causes instability at high channel density?
Most likely causes: Power, thermal, or system-level constraints.
Check:
- Server and rack power headroom
- Thermal limits under sustained load
- PCIe contention
- Memory bandwidth saturation
Rule: High density requires balanced systems—not just more cards.
4. How to safely handle firmware and software updates?
Most likely risks: Version incompatibility and uncontrolled rollout.
Check:
- Version compatibility matrix
- Validation in a staging environment
- Rollback plan in place
Rule: Treat updates like infrastructure changes, not patches.
5. Why does encoding fail or stall intermittently?
Most likely causes: Input irregularities or resource exhaustion.
Check:
- Input stream integrity
- Supported codec profiles and parameters
- Burst load behavior
- Application logging and retry logic
Rule: Intermittent failures usually originate upstream of the VPU.
6. How do we scale deployments without introducing risk?
Most likely failure mode: Scaling too fast without validation.
Check:
- One workload or region scaled at a time
- Density and power assumptions revalidated
- Monitoring expanded with scale
Rule: Scale incrementally and validate at each step.
7. In a multi-vendor stack, who owns the issue?
Ownership follows the architecture.
Check:
- Hardware, firmware, SDK → NETINT
- Application logic and orchestration → customer or partner
- Shared workflows → coordinated escalation
Rule: Escalate based on root cause, not symptoms.
8. Why does performance differ between on-prem and cloud nodes?
Most likely causes: Infrastructure asymmetry.
Check:
- CPU generation differences
- PCIe and memory bandwidth
- Network latency between environments
- Cloud instance constraints
Rule: Hybrid performance must be evaluated per environment.
9. Why does orchestration behaves inconsistently in hybrid deployments?
Most likely causes: Assumptions of uniform infrastructure.
Check:
- API rate limits
- Storage and network I/O differences
- Failure and retry behavior per environment
Rule: Hybrid orchestration requires environment-specific tuning.
10. How do we prevent version drift in hybrid deployments?
Most common hybrid failure mode: Inconsistent firmware, drivers, or SDKs.
Check:
- Version parity across environments
- Compatibility confirmed before upgrades
- Staged rollout sequence documented
Rule: Control upgrades centrally and apply incrementally.
Product Overview FAQs
Common implementation questions
These questions address how Quadra VPUs fit into real production environments. They do not replace deployment planning or benchmarking.
Architecture & Scope
Q: What problem are Quadra VPUs designed to solve?
A: Quadra VPUs are purpose-built to handle video encoding and decoding at sustained scale. They replace CPU- or GPU-based video workloads where throughput, power efficiency, and operational predictability are limiting factors.
Q: Do Quadra VPUs replace CPUs or GPUs?
A: No. Quadra VPUs replace CPU or GPU resources only for video processing tasks. General compute, orchestration, AI training, and graphics workloads remain unchanged.
Q: Are VPUs a general-purpose accelerator?
A: No. Quadra VPUs are fixed-function video processors. They are designed exclusively for encoding, decoding, and video-adjacent processing—not general compute or model training.
Integration & Compatibility
Q: What problem are Quadra VPUs designed to solve?
A: Quadra VPUs are purpose-built to handle video encoding and decoding at sustained scale. They replace CPU- or GPU-based video workloads where throughput, power efficiency, and operational predictability are limiting factors.
Q: Do Quadra VPUs replace CPUs or GPUs?
A: No. Quadra VPUs replace CPU or GPU resources only for video processing tasks. General compute, orchestration, AI training, and graphics workloads remain unchanged.
Q: Are VPUs a general-purpose accelerator?
A: No. Quadra VPUs are fixed-function video processors. They are designed exclusively for encoding, decoding, and video-adjacent processing—not general compute or model training.
Integration & Compatibility
Q: How do Quadra VPUs integrate into existing video pipelines?
A: Quadra VPUs integrate at the encoding or decoding layer using standard video frameworks such as FFmpeg, GStreamer, and supported SDKs. Ingest, playback, orchestration, and monitoring systems remain intact.
Q: Do we need to rewrite our video workflow to use VPUs?
A: No. Quadra VPUs are designed for compute substitution, not workflow replacement. Existing pipelines remain unchanged while video processing is offloaded incrementally.
Q: Can VPUs be deployed alongside existing CPU and GPU infrastructure?
A: Yes. VPUs are typically introduced alongside existing infrastructure, enabling side-by-side testing, gradual traffic shifts, and rollback at every stage.
Deployment & Operations
Q: Do VPUs require a rip-and-replace migration?
A: No. Quadra VPUs are deployed incrementally. Production traffic can be shifted gradually while CPU/GPU paths remain available for rollback.
Q: How are VPUs monitored in production?
A: VPUs expose stream-level and system-level metrics that integrate into existing monitoring and observability stacks. Deployment does not require a proprietary control plane
Q: What happens if a VPU deployment does not meet expectations?
A: Rollback paths remain intact throughout testing and migration. If performance, cost, or stability targets are not met, traffic can be shifted back without disruption.
Performance & Scaling
Q: How does VPU performance scale as volume increases?
A: Quadra VPUs are designed for sustained load. Throughput, power consumption, and cost scale predictably as stream density increases.
Q: Are benchmark results representative of production performance?
A: Benchmark results are valid only when run under sustained load using real workloads and preserved rollback paths. Peak-only or demo-optimized benchmarks are not considered actionable.
Q: What workloads benefit most from VPUs?
A: High-density, always-on video workloads—such as live streaming, surveillance, cloud video services, and large-scale transcoding—benefit most from dedicated video processing.
Cloud, On-Prem, and Hybrid
Q: Can Quadra VPUs be deployed in the cloud?
A: Yes. Quadra VPUs are available through supported cloud providers, enabling testing and deployment without owning hardware.
Q: Are VPUs limited to cloud environments?
A: No. Quadra VPUs are deployed across cloud, on-prem, hybrid, and edge environments depending on architectural requirements.
Q: Can the same deployment model be used across environments?
A: Yes. Deployment patterns remain consistent across cloud and on-prem environments, enabling predictable scaling and operational control.
Decision & Ownership
Q: Who typically owns a VPU deployment decision?
A: Successful deployments involve engineering, operations, and leadership alignment. Technical validation, operational control, and executive approval are addressed sequentially through the Deployment Playbook.
Q: Is deploying VPUs a “bet-the-company” decision?
A: No. Quadra VPUs are introduced incrementally, measured continuously, and scaled only after stability and cost behavior are validated.
Q: Where should teams start if they want to evaluate VPUs?
A: Teams should begin by establishing deployment expectations and validating benchmarks under production-like conditions before considering migration.
See Deployment Expectations >
Review the VPU Deployment Playbook >
Executive FAQs
Common leadership questions about deploying VPUs
These questions address risk, cost behavior, and decision ownership. They are intended to support internal alignment, not replace technical validation.
Strategy & Risk
Q: Is deploying VPUs a strategic platform decision?
A: No. VPU adoption is a compute substitution decision, not a platform commitment. It affects how video workloads are processed—not how applications, data, or cloud strategy are structured.
Q: Does this create vendor lock-in?
A: No. VPUs integrate into standard video frameworks and existing infrastructure. Pipelines, orchestration, and monitoring remain owned by your team, and rollback paths remain intact.
Q: What is the downside risk if this doesn’t work?
A: Deployment is incremental and reversible. If benchmarks or production behavior do not meet expectations, traffic can be shifted back to existing CPU or GPU paths without disruption.
Financial & Operational Impact
Q: How does this change our cost structure?
A: VPUs convert variable, unpredictable video compute costs into measurable, density-driven costs. Financial impact is evaluated through sustained-load benchmarks before production commitment.
Q: Does this reduce infrastructure complexity?
A: In production environments, VPUs typically reduce operational overhead by stabilizing video throughput and power behavior. They do not introduce new orchestration layers or proprietary control systems.
Q: How does this affect power and data center planning?
A: VPUs provide predictable power-per-stream behavior under sustained load, enabling more accurate capacity planning compared to general-purpose compute.
Governance & Approval
Q: Who typically approves a VPU deployment?
A: Successful deployments involve technical validation by engineering, operational readiness by infrastructure teams, and final approval by executive leadership once risk and rollback are clearly defined.
Q: How long does it take to reach a confident decision?
A: Decision timelines vary, but teams typically reach clarity after completing sustained benchmarks, tooling validation, and a defined migration plan—before large-scale production traffic is introduced.
Q: Is this an “early adopter” technology?
A: No. VPUs are deployed today in production environments where video throughput, cost predictability, and operational stability are business-critical.
Organizational Impact
Q: Does this require retraining teams?
A: No. Existing teams continue to use familiar tools and workflows. VPUs change the execution layer for video processing—not the operating model.
Q: Does this increase operational risk?
A: No. Deployment processes are designed to reduce risk by preserving rollback, maintaining observability, and preventing forced cutovers.
Q: What signals indicate readiness to scale?
A: Readiness is established when performance, cost behavior, and operational stability remain consistent under sustained production load—not during short-term tests or demos.