吴恩达亲自推的实战课，教你用Cerebras芯片让LLM推理快几倍，还能做实时翻译和语音agent，想上手硬核加速的别错过。
Andrew Ng联合Cerebras推出新课程，教授如何通过推理优化硬件加速LLM应用。课程聚焦Cerebras Wafer-Scale Engine，该硬件通过减少权重搬运实现比典型GPU快数倍的token生成。学员将对比GPU、TPU和Cerebras的架构差异，并动手构建网页个性化、市场信号多步分析等实时应用。课程还涵盖利用快速推理优化代理编码工作流，适合延迟敏感场景。
New course: Build LLM applications that respond to user requests quickly by running on hardware desi...
New course: Build LLM applications that respond to user requests quickly by running on hardware designed for fast inference. This short course was built with @Cerebras and taught by @zhennydez , @duerr_seb , and @MilksandMatcha . When a model generates text, much of the time is spent moving its weights out of memory and into the compute units. Inference-optimized hardware minimizes that movement, making token generation several times faster than on a typical GPU setup. In this course, the hardware you'll use is Cerebras' Wafer-Scale Engine, which is designed for fast inference by keeping the model's weights close to the compute units. Fast inference makes lengthy agentic workflows go faster, and also unlocks latency-sensitive, real-time applications like live translation and voice agents. Skills you'll gain: - Compare how GPUs, TPUs, and Cerebras' Wafer-Scale Engine each handle the memory-to-compute bottleneck - Build real-time applications powered by fast inference, including personalizing a webpage and running a multi-step workflow to analyze market signals - Adopt concrete habits for agentic coding with fast inference, keeping your sessions focused and steering the model more effectively My teams use Cerebras for several applications that are latency sensitive. Join and build LLM applications that respond quickly: deeplearning.ai/courses/fast-l… Your browser does not support the video tag. 🔗 View on Twitter 💬 3 🔄 0 ❤️ 7 👀 3056 📊 4 ⚡