According to AndrewYNg on X, DeepLearning.AI launched a new course, Efficient Inference with SGLang: Text and Image Generation, created with LMSys and RadixArk and taught by Richard Chen of RadixArk. As reported by AndrewYNg, the course targets production LLM cost bottlenecks and latency using SGLang techniques such as kernel fusion, paged attention, continuous batching, and optimized KV cache management for both text and image generation. According to AndrewYNg, the curriculum emphasizes practical deployment patterns for serving large models at scale, highlighting business value through reduced GPU hours, higher throughput per dollar, and improved tail latency—key metrics for inference economics.

According to DeepLearningAI on Twitter, a new course titled Efficient Inference with SGLang: Text and Image Generation is now live, focusing on cutting LLM inference costs by eliminating redundant computation using KV cache and RadixAttention (source: DeepLearning.AI tweet on April 8, 2026). As reported by DeepLearning.AI, the curriculum demonstrates how SGLang accelerates both text and image generation by reusing key value states to reduce recomputation and applying RadixAttention to optimize attention paths for lower latency and memory usage. According to DeepLearning.AI, the course also translates these techniques to vision and diffusion-style workloads, indicating practical deployment benefits such as higher throughput per GPU and reduced serving costs for production inference. As reported by DeepLearning.AI, the material targets practitioners aiming to improve utilization on commodity GPUs and scale serving capacity without proportional hardware spend.