CPU Overload Despite Having iGPU: Here's Why?

2026-06-09 · Source: Artificial Intelligence (AI) articles · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, short

Summary

Intel® Deep Learning Streamer (DL Streamer) is an open-source, GStreamer-based framework designed to optimize real-time computer vision analytics pipelines on Intel hardware, including iGPUs, NPUs, and dedicated hardware decoders. Released in November 2025, it addresses CPU overload issues common in multi-stream RTSP camera setups by offloading decoding, preprocessing, and inference tasks to the iGPU. DL Streamer integrates with OpenVINO™ Toolkit and supports hardware acceleration across Intel® Core™, Xeon®, Arc™, and Data Center GPU Flex Series devices. It simplifies development through modular components, enabling scalable solutions for real-time performance and efficient hardware utilization, as demonstrated by evolving pipeline configurations from CPU-only to full iGPU acceleration.

Key takeaway

For AI Engineers building real-time video analytics solutions, especially at the edge, you should adopt Intel® Deep Learning Streamer. This framework enables you to offload CPU-intensive decoding, preprocessing, and inference tasks entirely to your integrated GPU, significantly reducing CPU load and power consumption. By leveraging DL Streamer, you can achieve higher power efficiency, lower thermal stress, and true real-time performance without relying on costly discrete GPUs or cloud infrastructure.

Key insights

Intel DL Streamer efficiently offloads video analytics tasks to iGPUs, freeing CPUs and optimizing real-time performance.

Principles

• Full hardware acceleration is crucial for real-time video analytics.
• Minimize CPU-GPU memory transfers for optimal performance.
• Modular frameworks simplify complex AI pipeline development.

Method

Build real-time video analytics pipelines using DL Streamer's GStreamer elements, configuring "decodebin3", "vappostproc", and "gvadetect" to utilize iGPU for decoding, preprocessing, and inference.

In practice

• Implement full iGPU acceleration for RTSP video feeds.
• Use "decodebin3" for hardware-accelerated video decoding.
• Configure "gvadetect" for GPU-based inference.

Topics

• Real-time Video Analytics
• Edge AI
• Intel DL Streamer
• Integrated GPU
• OpenVINO Toolkit
• GStreamer

Code references

• dlstreamer/dlstreamer

Best for: Computer Vision Engineer, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence (AI) articles.