New course on serving LLMs efficiently -- how do you serve models to many concurrent users at low latency and reasonable cost? This short course is built with @RedHat and taught by @cedricclyburn. Efficient LLM serving requires efficient memory management. A 70B-parameter model takes ~140 GB just to load the weights. On top of that, every active request needs its own chunk of GPU memory, the KV cache, to store the token context it has built up so far. In this course, you'll learn to reduce a model's memory footprint with quantization and serve it using vLLM, which handles many concurrent requests efficiently through smart memory management. Skills you'll gain: - Quantize a model and measure the accuracy tradeoff - Serve a model with vLLM and watch it handle concurrent requests efficiently - Benchmark your deployment and make informed tradeoffs between speed, cost, and accuracy Join and learn to serve LLMs efficiently: https://www.deeplearning.ai/courses/fast-and-efficient-llm-inference-with-vllm· 高效服务 LLMs 的新课程

<p>New course on serving LLMs efficiently -- how do you serve models to many concurrent users at low latency and reasonable cost? This short course is built with <a href="https://rss.xcancel.com/RedHat" title="Red Hat">@RedHat</a> and taught by <a href="https://rss.xcancel.com/cedricclyburn" title="Cedric Clyburn">@cedricclyburn</a>.<br> <br> Efficient LLM serving requires efficient memory managem