Andrew Ng: Launches vLLM LLM Serving Course

Andrew Ng launched a short course with Red Hat on efficient LLM serving that covers KV cache memory management techniques in transformer model serving alongside the history and technical architecture of vLLM LLM inference engine, showing how a 70B-parameter model consumes roughly 140 GB just for weights while each concurrent request demands additional GPU memory for its KV cache.