Andrew Ng's New Course: Fast Inference Hardware
AndrewYNg · x · 2026-07-17
Andrew Ng launched a new course on building faster LLM applications using hardware designed for rapid inference to respond to user requests. Supported by Cerebras, the course is taught by @zhennydez, @duerrseb, and @MilksandMatcha.
The curriculum focuses on the memory-to-compute bottleneck during inference: when a model generates text, significant time is spent moving weights from memory to compute units. Cerebras's Wafer-Scale Engine minimizes this data transfer by keeping weights as close to the compute units as possible, enabling significantly faster token generation than standard GPU setups.
The course also covers:
- Differences between GPUs, TPUs, and Cerebras regarding this bottleneck
- Using faster inference for real-time applications like web personalization and multi-step market signal analysis
- Specific habits for agentic coding: keeping sessions focused and guiding the model more effectively
He also mentioned that his team has already been using Cerebras in multiple low-latency scenarios.
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