New course teaches efficient techniques for serving language models in production

A course on deploying Large Language Models efficiently has been launched through a collaboration between Red Hat and instructor Cedric Clyburn. The program addresses practical challenges of running models in production, with emphasis on GPU memory management. A 70-billion parameter model requires roughly 140 GB for its weights alone, while each active request demands additional memory to store the token context via the KV cache. The curriculum covers quantization methods to reduce memory footprint and features vLLM, a framework built to handle many concurrent requests simultaneously. Participants learn to quantize models, assess accuracy tradeoffs, serve models using vLLM, and benchmark deployments for performance and cost optimization.