GPU vs. CPU. How to Cut Live Streaming and AI Processing Costs? (www.red5.net)

Businesses are increasingly exploring how to cut costs in live streaming and AI processing, particularly when deciding between using GPUs or CPUs for these tasks. While CPUs offer a lower upfront cost (under $1 per instance) and are optimized for serial processing, GPUs, which excel at parallel execution for large data sets, can range from $3 to over $10 per instance. The disparity in pricing can significantly impact operating costs, especially as AI models become integral in real-time streaming workflows. Notably, the use of GPUs is often necessary for heavy encoding and processing tasks, but there are opportunities to leverage CPU capabilities to minimize costs while still meeting performance benchmarks, such as maintaining a latency under 250ms. Red5 offers solutions for real-time interactive streaming that challenge the notion that high costs are a barrier to scalable streaming solutions. By using CPU-based architectures, Red5 supports various real-time use cases while ensuring quality and low latency. The blog emphasizes the importance of strategic choices in encoding methods and the integration of advanced AI processing without compromising performance. As the market evolves with new entrants and fluctuating hardware costs, understanding the trade-offs between CPU and GPU deployment will be crucial for optimizing live streaming and AI processing costs effectively.