Predictable AI Performance Starts with the Operating System

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Discover how QNX SDP 8.x and PREEMPT_RT Linux performed in a controlled benchmark of embedded NPU inference on the Hailo-8 AI HAT+.
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Why Determinism Matters for Physical AI
Physical AI systems must do more than process data quickly. When inference informs the behaviour of a vehicle, robot, medical device, or industrial machine, engineering teams also need consistent, predictable timing. This whitepaper examines how the operating system affects NPU inference performance when the hardware, AI model, runtime, dataset, compiler, application logic, and cooling configuration remain constant.

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4.1%
Higher throughput
More frames processed per second.
3.9%
Lower end-to-end latency
Lower average end-to-end latency.
14x
Tighter consistency
Less variation across test runs.

You’ll Learn:

  • How the operating system influences inference performance, including throughput, end-to-end latency, and inference-only latency.
  • Why timing consistency matters for Physical AI, particularly when teams must define timing budgets and support validation or assurance arguments.
  • How QNX and PREEMPT_RT Linux compared across one million measured inferences per operating system on the same hardware and workload.
Download the White Paper
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QNX and Hailo Join Forces to Advance Reliable Physical AI at the Edge

By bringing support for the Hailo-8 AI Accelerator to QNX SDP 8.0, QNX and Hailo are expanding the hardware options available to developers building AI-powered edge systems where reliability and predictable performance are essential. Read the full press release to learn more about the collaboration.
Read the Press Release
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Want to Learn More?

Contact us to explore the benchmark methodology, results, and implications for engineers building predictable Physical AI systems in detail.
Contact Us
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