Speedata Presents - One Chip Cant Do It All: The New AI Tech Stack

The right chip for the right workload: APUs, GPUs, TPUs, and LPUs each serve a different purpose. Learn where each one fits. The AI hardware stack is going heterogeneous. As inference splits from training, as LLMs get grounded by real-time SQL, and as AI agents begin generating more queries than humans ever did, the question is no longer which processor is best, it's which processor is best for what. GPUs ignited the AI era. TPUs scaled model training. LPUs are optimizing inference latency. But as AI-native workloads drive exponential growth in SQL query volume and data pipeline complexity, a critical gap emerged, the data layer that feeds AI models had no purpose-built silicon until the APU. Speedata presented a live breakdown of the new AI tech stack where each processor fits, where it doesn't, and why the architecture decisions you make today will define your AI infrastructure economics for years to come. We covered: 🔳 How GPUs, TPUs, LPUs, and APUs map to different stages of the AI compute pipeline 🔳 Why AI agents and text-to-SQL are turning query volume into an infrastructure problem 🔳 Where the APU fits - from AI data preparation and feature engineering to large-scale batch analytics 🔳 A live look at two AI use cases: agentic AI workloads and AI data prep #AI #GPU #ApacheSpark